Enhancing Environmental Robustness in Few-shot Learning via Conditional Representation Learning | Paper Citation Record

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This paper cites Deep residual learning for image recognition,.

Enhancing Environmental Robustness in Few-shot Learning via Conditional Representation Learning Deep residual learning for image recognition,

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This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Enhancing Environmental Robustness in Few-shot Learning via Conditional Representation Learning An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

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This paper cites Scaling vision transformers,.

Enhancing Environmental Robustness in Few-shot Learning via Conditional Representation Learning Scaling vision transformers,

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This paper cites Pali: A jointly-scaled multilingual language-image model,.

Enhancing Environmental Robustness in Few-shot Learning via Conditional Representation Learning Pali: A jointly-scaled multilingual language-image model,

Reference 4

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This paper cites Matching networks for one shot learning,.

Enhancing Environmental Robustness in Few-shot Learning via Conditional Representation Learning Matching networks for one shot learning,

Reference 5

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

Enhancing Environmental Robustness in Few-shot Learning via Conditional Representation Learning Prototypical networks for few- shot learning,

Reference 6

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

Enhancing Environmental Robustness in Few-shot Learning via Conditional Representation Learning Model-agnostic meta-learning for fast adaptation of deep networks,

Reference 7

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

Enhancing Environmental Robustness in Few-shot Learning via Conditional Representation Learning A closer look at few-shot classification,

Reference 8

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This paper cites Relationnet: Learning deep-aligned representation for semantic image segmentation,.

Enhancing Environmental Robustness in Few-shot Learning via Conditional Representation Learning Relationnet: Learning deep-aligned representation for semantic image segmentation,

Reference 9

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This paper cites Joint distribution mat- ters: Deep brownian distance covariance for few-shot classification,.

Enhancing Environmental Robustness in Few-shot Learning via Conditional Representation Learning Joint distribution mat- ters: Deep brownian distance covariance for few-shot classification,

Reference 10

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

Enhancing Environmental Robustness in Few-shot Learning via Conditional Representation Learning Deepemd: Differentiable earth mover’s distance for few-shot learning,

Reference 11

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This paper cites Exploring complementary strengths of invariant and equivariant representations for few-shot learning,.

Enhancing Environmental Robustness in Few-shot Learning via Conditional Representation Learning Exploring complementary strengths of invariant and equivariant representations for few-shot learning,

Reference 12

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This paper cites Rankdnn: Learning to rank for few-shot learning,.

Enhancing Environmental Robustness in Few-shot Learning via Conditional Representation Learning Rankdnn: Learning to rank for few-shot learning,

Reference 13

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

Enhancing Environmental Robustness in Few-shot Learning via Conditional Representation Learning Styleadv: Meta style adversarial training for cross-domain few-shot learning,

Reference 14

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This paper cites Multi-layer tuning CLIP for few-shot image classification,.

Enhancing Environmental Robustness in Few-shot Learning via Conditional Representation Learning Multi-layer tuning CLIP for few-shot image classification,

Reference 15

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This paper cites At- tribute surrogates learning and spectral tokens pooling in transformers for few-shot learning,.

Enhancing Environmental Robustness in Few-shot Learning via Conditional Representation Learning At- tribute surrogates learning and spectral tokens pooling in transformers for few-shot learning,

Reference 16

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

Enhancing Environmental Robustness in Few-shot Learning via Conditional Representation Learning Pushing the limits of simple pipelines for few-shot learning: External data and fine-tuning make a difference,

Reference 17

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

Enhancing Environmental Robustness in Few-shot Learning via Conditional Representation Learning A broader study of cross-domain few-shot learning,

Reference 18

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This paper cites Revisiting pose- normalization for fine-grained few-shot recognition,.

Enhancing Environmental Robustness in Few-shot Learning via Conditional Representation Learning Revisiting pose- normalization for fine-grained few-shot recognition,

Reference 19

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This paper cites Generalization of model- agnostic meta-learning algorithms: Recurring and unseen tasks,.

Enhancing Environmental Robustness in Few-shot Learning via Conditional Representation Learning Generalization of model- agnostic meta-learning algorithms: Recurring and unseen tasks,

Reference 20

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This paper cites Bi-level meta-learning for few-shot domain generalization,.

Enhancing Environmental Robustness in Few-shot Learning via Conditional Representation Learning Bi-level meta-learning for few-shot domain generalization,

Reference 21

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This paper cites From sample poverty to rich feature learning: A new metric learning method for few-shot classification,.

Enhancing Environmental Robustness in Few-shot Learning via Conditional Representation Learning From sample poverty to rich feature learning: A new metric learning method for few-shot classification,

Reference 22

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This paper cites Bridging the gap between few- shot and many-shot learning via distribution calibration,.

Enhancing Environmental Robustness in Few-shot Learning via Conditional Representation Learning Bridging the gap between few- shot and many-shot learning via distribution calibration,

Reference 23

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This paper cites Variational feature disentangling for fine-grained few-shot classification,.

Enhancing Environmental Robustness in Few-shot Learning via Conditional Representation Learning Variational feature disentangling for fine-grained few-shot classification,

Reference 24

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This paper cites A comprehen- sive survey of few-shot learning: Evolution, applications, challenges, and opportunities,.

Enhancing Environmental Robustness in Few-shot Learning via Conditional Representation Learning A comprehen- sive survey of few-shot learning: Evolution, applications, challenges, and opportunities,

Reference 25

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This paper cites Plug- and-play feature generation for few-shot medical image classification,.

Enhancing Environmental Robustness in Few-shot Learning via Conditional Representation Learning Plug- and-play feature generation for few-shot medical image classification,

Reference 26

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This paper cites Few-shot classification of screen defects with class-agnostic mask and context-based classifier,.

Enhancing Environmental Robustness in Few-shot Learning via Conditional Representation Learning Few-shot classification of screen defects with class-agnostic mask and context-based classifier,

Reference 27

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This paper cites An aggregated loss function based lightweight few shot model for plant leaf disease classification,.

Enhancing Environmental Robustness in Few-shot Learning via Conditional Representation Learning An aggregated loss function based lightweight few shot model for plant leaf disease classification,

Reference 28

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This paper cites Cross-domain few- shot hyperspectral image classification with bias diminishing and domain bridging,.

Enhancing Environmental Robustness in Few-shot Learning via Conditional Representation Learning Cross-domain few- shot hyperspectral image classification with bias diminishing and domain bridging,

Reference 29

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This paper cites Boosting few-shot fine-grained recognition with background suppression and foreground alignment,.

Enhancing Environmental Robustness in Few-shot Learning via Conditional Representation Learning Boosting few-shot fine-grained recognition with background suppression and foreground alignment,

Reference 30

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This paper cites Low-rank pairwise alignment bilinear network for few-shot fine-grained image classifica- tion,.

Enhancing Environmental Robustness in Few-shot Learning via Conditional Representation Learning Low-rank pairwise alignment bilinear network for few-shot fine-grained image classifica- tion,

Reference 31

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This paper cites An adversarial meta-training framework for cross- domain few-shot learning,.

Enhancing Environmental Robustness in Few-shot Learning via Conditional Representation Learning An adversarial meta-training framework for cross- domain few-shot learning,

Reference 32

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This paper cites FHIST: A Benchmark for Few-shot Classification of Histological Images.

Enhancing Environmental Robustness in Few-shot Learning via Conditional Representation Learning FHIST: A Benchmark for Few-shot Classification of Histological Images

Reference 33

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This paper cites Learning representations by graphical mutual information estimation and maximization,.

Enhancing Environmental Robustness in Few-shot Learning via Conditional Representation Learning Learning representations by graphical mutual information estimation and maximization,

Reference 34

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This paper cites A simple frame- work for contrastive learning of visual representations,.

Enhancing Environmental Robustness in Few-shot Learning via Conditional Representation Learning A simple frame- work for contrastive learning of visual representations,

Reference 35

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This paper cites Masked autoencoders are scalable vision learners,.

Enhancing Environmental Robustness in Few-shot Learning via Conditional Representation Learning Masked autoencoders are scalable vision learners,

Reference 36

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This paper cites Unsupervised representation learning by predicting image rotations,.

Enhancing Environmental Robustness in Few-shot Learning via Conditional Representation Learning Unsupervised representation learning by predicting image rotations,

Reference 37

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This paper cites Unsupervised learning of visual represen- tations by solving jigsaw puzzles,.

Enhancing Environmental Robustness in Few-shot Learning via Conditional Representation Learning Unsupervised learning of visual represen- tations by solving jigsaw puzzles,

Reference 38

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This paper cites Boosting few-shot visual learning with self-supervision,.

Enhancing Environmental Robustness in Few-shot Learning via Conditional Representation Learning Boosting few-shot visual learning with self-supervision,

Reference 39

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This paper cites Pareto self- supervised training for few-shot learning,.

Enhancing Environmental Robustness in Few-shot Learning via Conditional Representation Learning Pareto self- supervised training for few-shot learning,

Reference 40

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This paper cites Learning a few-shot embedding model with contrastive learning,.

Enhancing Environmental Robustness in Few-shot Learning via Conditional Representation Learning Learning a few-shot embedding model with contrastive learning,

Reference 41

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This paper cites Partner-assisted learning for few-shot image classification,.

Enhancing Environmental Robustness in Few-shot Learning via Conditional Representation Learning Partner-assisted learning for few-shot image classification,

Reference 42

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This paper cites Crosstransformers: spatially- aware few-shot transfer,.

Enhancing Environmental Robustness in Few-shot Learning via Conditional Representation Learning Crosstransformers: spatially- aware few-shot transfer,

Reference 43

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This paper cites Few-shot classification with contrastive learning,.

Enhancing Environmental Robustness in Few-shot Learning via Conditional Representation Learning Few-shot classification with contrastive learning,

Reference 44

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

Enhancing Environmental Robustness in Few-shot Learning via Conditional Representation Learning Imagenet: A large-scale hierarchical image database,

Reference 45

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This paper cites Camouflaged object detection,.

Enhancing Environmental Robustness in Few-shot Learning via Conditional Representation Learning Camouflaged object detection,

Reference 46

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Enhancing Environmental Robustness in Few-shot Learning via Conditional Representation Learning Unresolved cited work

Reference 47

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This paper cites Aistudio,.

Enhancing Environmental Robustness in Few-shot Learning via Conditional Representation Learning Aistudio,

Reference 48

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This paper cites A realistic synthetic mushroom scenes dataset,.

Enhancing Environmental Robustness in Few-shot Learning via Conditional Representation Learning A realistic synthetic mushroom scenes dataset,

Reference 49

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This paper cites IP102: A large-scale benchmark dataset for insect pest recognition,.

Enhancing Environmental Robustness in Few-shot Learning via Conditional Representation Learning IP102: A large-scale benchmark dataset for insect pest recognition,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:21:16.592989Z

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Observation 8155dde7-e7f0-46c4-a681-c36f7fe110b7 · outbound

This paper cites An open access repository of images on plant health to enable the development of mobile disease diagnostics.

Enhancing Environmental Robustness in Few-shot Learning via Conditional Representation Learning An open access repository of images on plant health to enable the development of mobile disease diagnostics

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Observation c53d7111-ebe5-44fa-a5a4-2ad4807da16c · outbound

This paper cites Oracle-MNIST: a Dataset of Oracle Characters for Benchmarking Machine Learning Algorithms.

Enhancing Environmental Robustness in Few-shot Learning via Conditional Representation Learning Oracle-MNIST: a Dataset of Oracle Characters for Benchmarking Machine Learning Algorithms

Reference 52

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Observation ba0354a0-5d23-4ffa-bacc-249f327a7b5a · outbound

This paper cites Graph attention networks,.

Enhancing Environmental Robustness in Few-shot Learning via Conditional Representation Learning Graph attention networks,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:21:16.573632Z

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This paper cites V4D: 4d convolutional neural networks for video-level representation learning,.

Enhancing Environmental Robustness in Few-shot Learning via Conditional Representation Learning V4D: 4d convolutional neural networks for video-level representation learning,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:21:16.552577Z

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Observation fa43c93a-4275-4f54-8735-9d7fb67a84f7 · outbound

This paper cites ESPT: A self- supervised episodic spatial pretext task for improving few-shot learning,.

Enhancing Environmental Robustness in Few-shot Learning via Conditional Representation Learning ESPT: A self- supervised episodic spatial pretext task for improving few-shot learning,

Reference 55

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Observation 09e4e86d-9f04-48fc-9194-5c9f52087bd6 · outbound

This paper cites Learning to propagate labels: Transductive propagation network for few-shot learning,.

Enhancing Environmental Robustness in Few-shot Learning via Conditional Representation Learning Learning to propagate labels: Transductive propagation network for few-shot learning,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:21:16.505004Z

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Observation f4bf40f4-df38-48fb-8d10-50aae0a190d7 · outbound

This paper cites Parameterless transductive feature re-representation for few-shot learning,.

Enhancing Environmental Robustness in Few-shot Learning via Conditional Representation Learning Parameterless transductive feature re-representation for few-shot learning,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:21:16.481410Z

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Observation 1b822de1-a1e0-44cb-b31b-e84e00857a5b · outbound

This paper cites Easy - ensemble augmented-shot-y-shaped learning: State- of-the-art few-shot classification with simple components,.

Enhancing Environmental Robustness in Few-shot Learning via Conditional Representation Learning Easy - ensemble augmented-shot-y-shaped learning: State- of-the-art few-shot classification with simple components,

Reference 58

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Observation 1ab14bf6-94c5-44fe-b58d-1e4ced2ba361 · outbound

This paper cites Transductive few-shot learning with prototype- based label propagation by iterative graph refinement,.

Enhancing Environmental Robustness in Few-shot Learning via Conditional Representation Learning Transductive few-shot learning with prototype- based label propagation by iterative graph refinement,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:21:16.435679Z

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Observation feb5e3ea-cca5-4f6f-aecf-a680dade91e4 · outbound

This paper cites Feature mixture on pre-trained model for few-shot learning,.

Enhancing Environmental Robustness in Few-shot Learning via Conditional Representation Learning Feature mixture on pre-trained model for few-shot learning,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:21:16.412678Z

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Observation fafb55c5-e075-4826-a9ab-2fd2cf568d27 · outbound

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

Enhancing Environmental Robustness in Few-shot Learning via Conditional Representation Learning Few-shot learning via embedding adaptation with set-to-set functions,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:21:16.388875Z

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Observation a1dbe0e6-7684-4e5b-ac48-8bc97bb8356e · outbound

This paper cites Class-aware patch embedding adaptation for few-shot image classification,.

Enhancing Environmental Robustness in Few-shot Learning via Conditional Representation Learning Class-aware patch embedding adaptation for few-shot image classification,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:21:16.368983Z

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Observation eae44294-b548-4dad-9167-c3bbb98ed9c8 · outbound

This paper cites Learning transferable visual models from natural language supervi- sion,.

Enhancing Environmental Robustness in Few-shot Learning via Conditional Representation Learning Learning transferable visual models from natural language supervi- sion,

Reference 63

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Observation d1e09b71-cbd1-4b2e-8cf3-c2ead24aa327 · outbound

This paper cites Clip-adapter: Better vision-language models with feature adapters,.

Enhancing Environmental Robustness in Few-shot Learning via Conditional Representation Learning Clip-adapter: Better vision-language models with feature adapters,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:21:16.336124Z

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Observation f9cd6538-31c9-4136-a0a0-c3987055bc8c · outbound

This paper cites Learning deep features for discriminative localization,.

Enhancing Environmental Robustness in Few-shot Learning via Conditional Representation Learning Learning deep features for discriminative localization,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:21:16.315165Z

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Observation fbfc3d31-6dd6-4fd1-b9dd-ee988313fd04 · outbound

This paper cites 2774–2784.

Enhancing Environmental Robustness in Few-shot Learning via Conditional Representation Learning 2774–2784

Reference 2020

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:21:16.675841Z

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