Reliable Few-shot Learning under Dual Noises | Paper Citation Record

Observation 8a77557d-6e54-4b14-aaec-dcbd7106d1de · outbound

This paper cites Deta: Denoised task adaptation for few-shot learning,.

Reliable Few-shot Learning under Dual Noises Deta: Denoised task adaptation for few-shot learning,

Reference 1

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Observation acd90560-2d33-4a86-9b41-d5f0bde7e116 · outbound

This paper cites Prototypical networks for few-shot learning,.

Reliable Few-shot Learning under Dual Noises Prototypical networks for few-shot learning,

Reference 2

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Observation 9db4f961-e983-44af-a26a-986364f74f5b · outbound

This paper cites Masked autoencoders are scalable vision learners,.

Reliable Few-shot Learning under Dual Noises Masked autoencoders are scalable vision learners,

Reference 3

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Observation 3fbb244b-4185-4117-8e1c-e59dffcfc216 · outbound

This paper cites Adaptive cross-modal few-shot learning,.

Reliable Few-shot Learning under Dual Noises Adaptive cross-modal few-shot learning,

Reference 4

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Observation 9b693c6b-b5fb-4b1a-b603-2aa9688080a4 · outbound

This paper cites Flex: Unifying evalua- tion for few-shot nlp,.

Reliable Few-shot Learning under Dual Noises Flex: Unifying evalua- tion for few-shot nlp,

Reference 5

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Observation d6eb9d96-2d00-48aa-ae44-08e22a48f352 · outbound

This paper cites Model-agnostic meta-learning for fast adaptation of deep networks,.

Reliable Few-shot Learning under Dual Noises Model-agnostic meta-learning for fast adaptation of deep networks,

Reference 6

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Observation 6dc62983-34b2-4ba3-a462-a8c65bdd8dce · outbound

This paper cites Reinforced attention for few-shot learning and beyond,.

Reliable Few-shot Learning under Dual Noises Reinforced attention for few-shot learning and beyond,

Reference 7

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Observation c89f677e-8991-4cc8-b3ff-359dfb66c390 · outbound

This paper cites Meta-detr: Image- level few-shot detection with inter-class correlation exploitation,.

Reliable Few-shot Learning under Dual Noises Meta-detr: Image- level few-shot detection with inter-class correlation exploitation,

Reference 8

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Observation da14ddf6-5831-4ccd-9bd6-fc9b908200fe · outbound

This paper cites Meta-transfer learning through hard tasks,.

Reliable Few-shot Learning under Dual Noises Meta-transfer learning through hard tasks,

Reference 9

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Observation e10e6c42-8ce4-4320-82a3-99107652fd39 · outbound

This paper cites Revisiting unsupervised meta- learning via the characteristics of few-shot tasks,.

Reliable Few-shot Learning under Dual Noises Revisiting unsupervised meta- learning via the characteristics of few-shot tasks,

Reference 10

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Observation 719a7603-852f-43e3-b0be-024d2be5e21a · outbound

This paper cites A closer look at few-shot classification,.

Reliable Few-shot Learning under Dual Noises A closer look at few-shot classification,

Reference 11

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Observation 78c57f89-e053-467b-ab5f-f9cb79b94bb7 · outbound

This paper cites Universal representation learning from multiple domains for few-shot classification,.

Reliable Few-shot Learning under Dual Noises Universal representation learning from multiple domains for few-shot classification,

Reference 12

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Observation 8adfc63f-f0b5-43b2-ac87-de49d7cf5b09 · outbound

This paper cites Cross-domain few-shot learning with task-specific adapters,.

Reliable Few-shot Learning under Dual Noises Cross-domain few-shot learning with task-specific adapters,

Reference 13

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Observation 6fb3eb19-7ebd-4fc5-905a-000e8b814fe4 · outbound

This paper cites A closer look at few-shot classification again,.

Reliable Few-shot Learning under Dual Noises A closer look at few-shot classification again,

Reference 14

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Observation 45d2ec92-83ed-43f9-801a-17fc99bc1b24 · outbound

This paper cites Momentum con- trast for unsupervised visual representation learning,.

Reliable Few-shot Learning under Dual Noises Momentum con- trast for unsupervised visual representation learning,

Reference 15

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Observation b3156b47-238e-4cb9-8413-922c1348fef0 · outbound

This paper cites Transformer in transformer,.

Reliable Few-shot Learning under Dual Noises Transformer in transformer,

Reference 16

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Observation ceaa59a1-9888-4eff-9665-b29403cb1bc9 · outbound

This paper cites Swin transformer: Hierarchical vision transformer using shifted windows,.

Reliable Few-shot Learning under Dual Noises Swin transformer: Hierarchical vision transformer using shifted windows,

Reference 17

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Observation 96f07f21-c070-493a-8854-9dc693c5289a · outbound

This paper cites A baseline for few-shot image classification,.

Reliable Few-shot Learning under Dual Noises A baseline for few-shot image classification,

Reference 18

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Observation 55c867df-e3f9-45c9-b8e0-3d0e0ea03ed6 · outbound

This paper cites Pushing the limits of simple pipelines for few-shot learning: External data and fine-tuning make a difference,.

Reliable Few-shot Learning under Dual Noises Pushing the limits of simple pipelines for few-shot learning: External data and fine-tuning make a difference,

Reference 19

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Observation d028d6cd-106b-4542-b5fa-face81343baf · outbound

This paper cites Exploring efficient few-shot adaptation for vision transformers,.

Reliable Few-shot Learning under Dual Noises Exploring efficient few-shot adaptation for vision transformers,

Reference 20

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Observation eeb1d9aa-a5cd-42ee-8a8d-a1bb024b861e · outbound

This paper cites Prompt-aligned gradient for prompt tuning,.

Reliable Few-shot Learning under Dual Noises Prompt-aligned gradient for prompt tuning,

Reference 21

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Observation 30e9aaa8-a88d-4fa8-92f3-33a3ffb0c83e · outbound

This paper cites Prompt learning with optimal transport for vision-language models,.

Reliable Few-shot Learning under Dual Noises Prompt learning with optimal transport for vision-language models,

Reference 22

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Observation a7230652-2767-405f-a441-3d7f9efe2a16 · outbound

This paper cites Visual prompt tuning,.

Reliable Few-shot Learning under Dual Noises Visual prompt tuning,

Reference 23

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Observation 58843006-a61e-47cf-952b-c5ceab9694e1 · outbound

This paper cites Meta- dataset: A dataset of datasets for learning to learn from few examples,.

Reliable Few-shot Learning under Dual Noises Meta- dataset: A dataset of datasets for learning to learn from few examples,

Reference 24

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Observation 28574950-6ec1-4b69-bea0-190d01007b40 · outbound

This paper cites Classification with noisy labels by importance reweighting,.

Reliable Few-shot Learning under Dual Noises Classification with noisy labels by importance reweighting,

Reference 25

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Observation 69cbcd21-1d12-448e-8b54-9fbc94e3e8aa · outbound

This paper cites Learning from noisy labels with deep neural networks: A survey,.

Reliable Few-shot Learning under Dual Noises Learning from noisy labels with deep neural networks: A survey,

Reference 26

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Observation 0b8fe87b-4bf9-459f-8c9f-aab78c02cdfb · outbound

This paper cites Resolving training biases via influence-based data relabeling,.

Reliable Few-shot Learning under Dual Noises Resolving training biases via influence-based data relabeling,

Reference 27

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Observation cfec28c3-4097-403b-a4a8-c5313bde0246 · outbound

This paper cites Locoop: Few-shot out- of-distribution detection via prompt learning,.

Reliable Few-shot Learning under Dual Noises Locoop: Few-shot out- of-distribution detection via prompt learning,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:48:08.420019Z

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Observation 27ab79fc-35f0-4b3e-a5b7-bbe71a2e1a2f · outbound

This paper cites Libfewshot: A comprehensive library for few-shot learning,.

Reliable Few-shot Learning under Dual Noises Libfewshot: A comprehensive library for few-shot learning,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:48:08.411587Z

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Observation 9fe915e4-2137-43a9-9b0c-31661b6d8287 · outbound

This paper cites Few-shot object detection and viewpoint estimation for objects in the wild,.

Reliable Few-shot Learning under Dual Noises Few-shot object detection and viewpoint estimation for objects in the wild,

Reference 30

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Observation 9d9a0b24-0ba3-4f13-a3f0-90a13036b9d9 · outbound

This paper cites How to trust unlabeled data? instance credibility inference for few-shot learning,.

Reliable Few-shot Learning under Dual Noises How to trust unlabeled data? instance credibility inference for few-shot learning,

Reference 31

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Observation 6bb09d36-8bf2-4061-990f-291f0513b7f3 · outbound

This paper cites Knowledge-guided multi-label few-shot learning for general image recognition,.

Reliable Few-shot Learning under Dual Noises Knowledge-guided multi-label few-shot learning for general image recognition,

Reference 32

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Observation e19a0168-24cf-4d80-b4ae-ad46c3a384a1 · outbound

This paper cites On First-Order Meta-Learning Algorithms.

Reliable Few-shot Learning under Dual Noises On First-Order Meta-Learning Algorithms

Reference 33

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Observation 7f6e92fd-d49b-42d3-a542-38f331b72eff · outbound

This paper cites Meta networks,.

Reliable Few-shot Learning under Dual Noises Meta networks,

Reference 34

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Observation c520a5a9-66ba-4365-9ab5-0e3bb537a0b6 · outbound

This paper cites Cross attention network for few-shot classification,.

Reliable Few-shot Learning under Dual Noises Cross attention network for few-shot classification,

Reference 35

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Observation d660f81b-8701-40cf-ba12-81d5042115a9 · outbound

This paper cites Few-shot learning via embedding adaptation with set-to-set functions,.

Reliable Few-shot Learning under Dual Noises Few-shot learning via embedding adaptation with set-to-set functions,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:48:08.364748Z

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Observation faa6ee56-ec98-4419-a33c-08b961556655 · outbound

This paper cites Deepemd: Differentiable earth mover’s distance for few-shot learning,.

Reliable Few-shot Learning under Dual Noises Deepemd: Differentiable earth mover’s distance for few-shot learning,

Reference 37

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Observation e049904e-0bbc-4f00-b83b-bc40e1a03205 · outbound

This paper cites A broader study of cross- domain few-shot learning,.

Reliable Few-shot Learning under Dual Noises A broader study of cross- domain few-shot learning,

Reference 38

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Observation c9684349-27f1-46dc-a369-ca933fdb91bd · outbound

This paper cites Cross- domain few-shot classification via learned feature-wise transfor- mation,.

Reliable Few-shot Learning under Dual Noises Cross- domain few-shot classification via learned feature-wise transfor- mation,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:48:08.341164Z

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Observation cdef2925-02bb-4435-90d2-76dc46beda62 · outbound

This paper cites Boosting the generalization capability in cross-domain few-shot learning via noise-enhanced supervised autoencoder,.

Reliable Few-shot Learning under Dual Noises Boosting the generalization capability in cross-domain few-shot learning via noise-enhanced supervised autoencoder,

Reference 40

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Observation ffce574d-3bf6-4e9a-b772-6ee3428b4460 · outbound

This paper cites Tgdm: Target guided dynamic mixup for cross-domain few-shot learning,.

Reliable Few-shot Learning under Dual Noises Tgdm: Target guided dynamic mixup for cross-domain few-shot learning,

Reference 41

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Observation 23769b8c-d8ad-4c0c-9cee-16e3583370c7 · outbound

This paper cites Generalized meta-fdmixup: Cross-domain few-shot learning guided by labeled target data,.

Reliable Few-shot Learning under Dual Noises Generalized meta-fdmixup: Cross-domain few-shot learning guided by labeled target data,

Reference 42

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Observation c76dad6f-a308-4954-b8ac-7984421e07a7 · outbound

This paper cites Free-lunch for cross- domain few-shot learning: Style-aware episodic training with robust contrastive learning,.

Reliable Few-shot Learning under Dual Noises Free-lunch for cross- domain few-shot learning: Style-aware episodic training with robust contrastive learning,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:48:08.307930Z

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Observation 76b95ef4-0a36-4ba3-bd9e-d684e7ad8f94 · outbound

This paper cites Styleadv: Meta style ad- versarial training for cross-domain few-shot learning,.

Reliable Few-shot Learning under Dual Noises Styleadv: Meta style ad- versarial training for cross-domain few-shot learning,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:48:08.298536Z

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Observation 41b8aefc-a90e-48a2-8606-cdc99d330395 · outbound

This paper cites Fast and flexible multi-task classification using conditional neu- ral adaptive processes,.

Reliable Few-shot Learning under Dual Noises Fast and flexible multi-task classification using conditional neu- ral adaptive processes,

Reference 45

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Observation 3983298c-e001-4af7-82d6-e998d1a9e5da · outbound

This paper cites Improved few-shot visual classification,.

Reliable Few-shot Learning under Dual Noises Improved few-shot visual classification,

Reference 46

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Observation b6740522-8a19-45ca-882e-5b3c1d37ae51 · outbound

This paper cites Learning a universal template for few-shot dataset generaliza- tion,.

Reliable Few-shot Learning under Dual Noises Learning a universal template for few-shot dataset generaliza- tion,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:48:08.272852Z

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Observation 17622cca-5f67-4a1d-a624-9a5d59c6ce59 · outbound

This paper cites Dense classifi- cation and implanting for few-shot learning,.

Reliable Few-shot Learning under Dual Noises Dense classifi- cation and implanting for few-shot learning,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:48:08.264438Z

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Observation fdafd9b6-c427-4fae-9cd1-4c291d62aa90 · outbound

This paper cites Learning transferable visual models from natural language supervision,.

Reliable Few-shot Learning under Dual Noises Learning transferable visual models from natural language supervision,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:48:08.255290Z

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Observation 3dc9e79e-c99c-4ced-a4a1-93d61a6e19ff · outbound

This paper cites Learning to prompt for vision-language models,.

Reliable Few-shot Learning under Dual Noises Learning to prompt for vision-language models,

Reference 50

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Observation 081fe833-c918-4024-8493-b3df5f277220 · outbound

This paper cites Conditional prompt learning for vision-language models,.

Reliable Few-shot Learning under Dual Noises Conditional prompt learning for vision-language models,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:48:08.242073Z

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Observation 6074affd-a22c-4a30-8a02-c03e471026c0 · outbound

This paper cites Visual-language prompt tuning with knowledge-guided context optimization,.

Reliable Few-shot Learning under Dual Noises Visual-language prompt tuning with knowledge-guided context optimization,

Reference 52

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Observation fe24d075-cb9e-42f7-8e92-825aded8dd6e · outbound

This paper cites Dept: Decoupled prompt tuning,.

Reliable Few-shot Learning under Dual Noises Dept: Decoupled prompt tuning,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:48:08.229573Z

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Observation f3f91eb0-1d5e-4dbf-8db6-f4057578b222 · outbound

This paper cites A closer look at memorization in deep networks,.

Reliable Few-shot Learning under Dual Noises A closer look at memorization in deep networks,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:48:08.220864Z

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Observation bb968b7e-dfd6-4fe5-9b20-799d779ea239 · outbound

This paper cites Rectifying the shortcut learning of background for few-shot learning,.

Reliable Few-shot Learning under Dual Noises Rectifying the shortcut learning of background for few-shot learning,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:48:08.213023Z

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Observation 5bf8e534-7e90-4d41-ae2a-a078104668d9 · outbound

This paper cites Few-shot learning with noisy labels,.

Reliable Few-shot Learning under Dual Noises Few-shot learning with noisy labels,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:48:08.205053Z

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Observation 5b8ca764-c7b3-44fa-b02d-27296e482977 · outbound

This paper cites Deepemd: Few-shot image classification with differentiable earth mover’s distance and structured classifiers,.

Reliable Few-shot Learning under Dual Noises Deepemd: Few-shot image classification with differentiable earth mover’s distance and structured classifiers,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:48:08.196061Z

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Observation 10e74be8-997a-41a7-86da-2e27a8407a07 · outbound

This paper cites Toward open set recognition,.

Reliable Few-shot Learning under Dual Noises Toward open set recognition,

Reference 58

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Observation d39286b9-8efa-4393-8a9c-bfce83cc6492 · outbound

This paper cites Towards open world recognition,.

Reliable Few-shot Learning under Dual Noises Towards open world recognition,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:48:08.117171Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 99704880-53e9-45f6-837b-6143c3370315 · outbound

This paper cites Towards open set deep networks,.

Reliable Few-shot Learning under Dual Noises Towards open set deep networks,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:48:08.109634Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0377d49e-c8e5-4986-b845-f1e7aed81b0c · outbound

This paper cites Enhancing the reliability of out- of-distribution image detection in neural networks,.

Reliable Few-shot Learning under Dual Noises Enhancing the reliability of out- of-distribution image detection in neural networks,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:48:08.101200Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 15248b1a-18a9-4504-8b87-582ddb76d2c4 · outbound

This paper cites Energy-based out-of- distribution detection,.

Reliable Few-shot Learning under Dual Noises Energy-based out-of- distribution detection,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:48:08.092469Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1ddc1ad2-8079-4317-be01-fc755863cf28 · outbound

This paper cites A baseline for detecting misclassi- fied and out-of-distribution examples in neural networks,.

Reliable Few-shot Learning under Dual Noises A baseline for detecting misclassi- fied and out-of-distribution examples in neural networks,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:48:08.084399Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e88c5b50-ad6c-4149-9edf-e47dfb061d25 · outbound

This paper cites Mitigating neural network overconfidence with logit normalization,.

Reliable Few-shot Learning under Dual Noises Mitigating neural network overconfidence with logit normalization,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:48:08.076196Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ca995ea5-20a7-48fe-aa8e-ec0a7cc5cfd7 · outbound

This paper cites Dice: Leveraging sparsification for out-of- distribution detection,.

Reliable Few-shot Learning under Dual Noises Dice: Leveraging sparsification for out-of- distribution detection,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:48:08.066957Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 58a7c281-b808-4a0a-8295-190463a82ae9 · outbound

This paper cites Line: Out-of-distribution detection by leveraging important neurons,.

Reliable Few-shot Learning under Dual Noises Line: Out-of-distribution detection by leveraging important neurons,

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:48:08.057448Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0600c98f-75df-479d-a349-9aa61e74af2b · outbound

This paper cites From global to local: Multi-scale out-of-distribution detection,.

Reliable Few-shot Learning under Dual Noises From global to local: Multi-scale out-of-distribution detection,

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:48:08.048523Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9e3daee8-9369-4b2e-b746-45cd99c96257 · outbound

This paper cites Poodle: Improving few-shot learning via penalizing out-of-distribution samples,.

Reliable Few-shot Learning under Dual Noises Poodle: Improving few-shot learning via penalizing out-of-distribution samples,

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:48:08.039729Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 14464638-dec5-47cf-96af-c47f79b2aa97 · outbound

This paper cites Distance- based image classification: Generalizing to new classes at near- zero cost,.

Reliable Few-shot Learning under Dual Noises Distance- based image classification: Generalizing to new classes at near- zero cost,

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:48:08.031814Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b761a502-2abd-4174-a982-da64e505e68e · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale,.

Reliable Few-shot Learning under Dual Noises An image is worth 16x16 words: Transformers for image recognition at scale,

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:48:08.024129Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 518513ec-74c7-4d01-b68d-7c8e49f392a5 · outbound

This paper cites Do better imagenet models transfer better?.

Reliable Few-shot Learning under Dual Noises Do better imagenet models transfer better?

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:48:08.016632Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1b6c4fc1-cc0c-4d90-914b-f82412992293 · outbound

This paper cites A simple framework for contrastive learning of visual representations,.

Reliable Few-shot Learning under Dual Noises A simple framework for contrastive learning of visual representations,

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-06T23:48:07.620670Z

Source-reported events for the cited work

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Observation 2f391c16-4f37-468d-bd59-58fbe711da9c · outbound

This paper cites Learning contrastive embedding in low-dimensional space,.

Reliable Few-shot Learning under Dual Noises Learning contrastive embedding in low-dimensional space,

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:48:08.002474Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0f2a5e13-07bd-4021-a5ef-0ec93ce3ba86 · outbound

This paper cites Meta-learning with latent embedding optimization,.

Reliable Few-shot Learning under Dual Noises Meta-learning with latent embedding optimization,

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:48:07.994234Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 34fc4b42-8d91-46b9-883d-78c746ad697d · outbound

This paper cites Prototype rectification for few-shot learning,.

Reliable Few-shot Learning under Dual Noises Prototype rectification for few-shot learning,

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:48:07.986070Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9cfe9f35-aa5c-49f8-b13c-7c613dc478eb · outbound

This paper cites Alleviating the sample selection bias in few-shot learning by removing projection to the centroid,.

Reliable Few-shot Learning under Dual Noises Alleviating the sample selection bias in few-shot learning by removing projection to the centroid,

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:48:07.977269Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2eaa3076-83ad-4042-a09d-417a86d00283 · outbound

This paper cites Cutmix: Regularization strategy to train strong classifiers with localizable features,.

Reliable Few-shot Learning under Dual Noises Cutmix: Regularization strategy to train strong classifiers with localizable features,

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:48:07.967863Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3f8ddf6a-1345-493b-b3e2-0a7cd8de7306 · outbound

This paper cites Imagenet: A large-scale hierarchical image database,.

Reliable Few-shot Learning under Dual Noises Imagenet: A large-scale hierarchical image database,

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:48:07.959328Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 70cf523d-ead4-4acc-85ad-7b74cb5e04a7 · outbound

This paper cites A universal representation transformer layer for few-shot image classification,.

Reliable Few-shot Learning under Dual Noises A universal representation transformer layer for few-shot image classification,

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:48:07.950851Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation dd7da2f3-28d4-40c4-89bd-983c633f1ea1 · outbound

This paper cites Emerging properties in self-supervised vision transformers,.

Reliable Few-shot Learning under Dual Noises Emerging properties in self-supervised vision transformers,

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:48:07.942003Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 01a0d0ac-da58-46fe-8c3e-110f401e118b · outbound

This paper cites Training data-efficient image transformers-distillation through attention,.

Reliable Few-shot Learning under Dual Noises Training data-efficient image transformers-distillation through attention,

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:48:07.933143Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f24c85ac-f94c-42af-8fa1-3e25c3359690 · outbound

This paper cites Optimized Generic Feature Learning for Few-shot Classification across Domains.

Reliable Few-shot Learning under Dual Noises Optimized Generic Feature Learning for Few-shot Classification across Domains

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-06T23:48:07.644311Z

Source-reported events for the cited work

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Observation 55569570-3ad9-4c31-a88d-9eb86296a146 · outbound

This paper cites Enhanc- ing few-shot image classification with unlabelled examples,.

Reliable Few-shot Learning under Dual Noises Enhanc- ing few-shot image classification with unlabelled examples,

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:48:07.918894Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a307f589-0482-441e-9c4a-0a7ce9e52628 · outbound

This paper cites Selecting relevant features from a multi-domain representation for few-shot classification,.

Reliable Few-shot Learning under Dual Noises Selecting relevant features from a multi-domain representation for few-shot classification,

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:48:07.911508Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 24ad9c85-2908-41ce-9963-4b04cd39cd14 · outbound

This paper cites A multi- mode modulator for multi-domain few-shot classification,.

Reliable Few-shot Learning under Dual Noises A multi- mode modulator for multi-domain few-shot classification,

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:48:07.903957Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 729f14b8-ff03-4a6c-a411-d8e44cec8115 · outbound

This paper cites Supervised contrastive learning,.

Reliable Few-shot Learning under Dual Noises Supervised contrastive learning,

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:48:07.896259Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e8a37a14-5aa3-4723-bab8-8adc94961b87 · outbound

This paper cites The use of ranks to avoid the assumption of normality implicit in the analysis of variance,.

Reliable Few-shot Learning under Dual Noises The use of ranks to avoid the assumption of normality implicit in the analysis of variance,

Reference 88

Resolution: unresolved; no resolver link, observed 2026-08-06T23:48:07.655627Z

Source-reported events for the cited work

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Observation 27cb3ee9-fb64-45b5-bcc6-75ebfa8c2868 · outbound

This paper cites Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power,.

Reliable Few-shot Learning under Dual Noises Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power,

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:48:07.888553Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 49e46673-4ff6-416d-a0b4-c3adc2705efc · outbound

This paper cites Skip tuning: Pre-trained vision-language models are effective and efficient adapters themselves,.

Reliable Few-shot Learning under Dual Noises Skip tuning: Pre-trained vision-language models are effective and efficient adapters themselves,

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:48:07.880609Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c97ed0bc-3289-458f-8d1b-373cfd31b278 · outbound

This paper cites Delving into out- of-distribution detection with vision-language representations,.

Reliable Few-shot Learning under Dual Noises Delving into out- of-distribution detection with vision-language representations,

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:48:07.871905Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c3a71b9d-5b91-4e99-bbe2-7e4df8606e2e · outbound

This paper cites Scaling for Training Time and Post-hoc Out-of-distribution Detection Enhancement.

Reliable Few-shot Learning under Dual Noises Scaling for Training Time and Post-hoc Out-of-distribution Detection Enhancement

Reference 92

Resolution: unresolved; no resolver link, observed 2026-08-06T23:48:07.666441Z

Source-reported events for the cited work

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Observation ad2d2852-b031-4201-a51f-32b11dd0c862 · outbound

This paper cites GL-MCM: Global and Local Maximum Concept Matching for Zero-Shot Out-of-Distribution Detection.

Reliable Few-shot Learning under Dual Noises GL-MCM: Global and Local Maximum Concept Matching for Zero-Shot Out-of-Distribution Detection

Reference 93

Resolution: unresolved; no resolver link, observed 2026-08-06T23:48:07.668972Z

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Unavailable: canonical work link unavailable.

Observation 568632da-9ca2-46d3-825d-98739054d029 · outbound

This paper cites Nearest neighbor guidance for out-of-distribution detection,.

Reliable Few-shot Learning under Dual Noises Nearest neighbor guidance for out-of-distribution detection,

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:48:07.863693Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c3a1cd68-0ecf-499b-8d34-1f266be2d9de · outbound

This paper cites Enhancing the reliability of out-of- distribution image detection in neural networks,.

Reliable Few-shot Learning under Dual Noises Enhancing the reliability of out-of- distribution image detection in neural networks,

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:48:07.854426Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3291d82e-d829-4568-bc59-0968e2ae0235 · outbound

This paper cites Vim: Out-of-distribution with virtual-logit matching,.

Reliable Few-shot Learning under Dual Noises Vim: Out-of-distribution with virtual-logit matching,

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:48:07.845545Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a775adfc-e7e2-4e2c-aa4f-3899beda9928 · outbound

This paper cites Out-of-distribution detection with deep nearest neighbors,.

Reliable Few-shot Learning under Dual Noises Out-of-distribution detection with deep nearest neighbors,

Reference 97

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:48:07.835923Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 24ab70b2-3f9e-4a4a-8fb9-32aa596fb04e · outbound

This paper cites Non-parametric outlier synthe- sis,.

Reliable Few-shot Learning under Dual Noises Non-parametric outlier synthe- sis,

Reference 98

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:48:07.827738Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 62029307-ee81-47e2-9b4b-2e16e4240d0a · outbound

This paper cites Mos: Towards scaling out-of-distribution detection for large semantic space,.

Reliable Few-shot Learning under Dual Noises Mos: Towards scaling out-of-distribution detection for large semantic space,

Reference 99

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:48:07.819033Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 308aa344-11d0-4ec7-a037-a5319fc29e49 · outbound

This paper cites The inaturalist species classification and detection dataset,.

Reliable Few-shot Learning under Dual Noises The inaturalist species classification and detection dataset,

Reference 100

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:48:07.810337Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4cac35f1-6313-42f1-af0f-c521223f0aca · outbound

This paper cites Sun database: Large-scale scene recognition from abbey to zoo,.

Reliable Few-shot Learning under Dual Noises Sun database: Large-scale scene recognition from abbey to zoo,

Reference 101

Resolution: unresolved; no resolver link, observed 2026-08-06T23:48:07.688205Z

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