Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection | Paper Citation Record

Observation 0a847a03-a10f-440e-8479-19c3dfc27653 · outbound

This paper cites Deep residual learning for image recognition,.

Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection Deep residual learning for image recognition,

Reference 1

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Observation 2011da0f-3700-4b63-bfff-ae3f82bcc23a · outbound

This paper cites Multi- similarity loss with general pair weighting for deep metric learning,.

Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection Multi- similarity loss with general pair weighting for deep metric learning,

Reference 2

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This paper cites Arcface: Additive angular margin loss for deep face recognition,.

Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection Arcface: Additive angular margin loss for deep face recognition,

Reference 3

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Observation 495a50a9-a0f4-49e1-8f0a-434ab9b034e7 · outbound

This paper cites Noise-resistant deep metric learning with ranking-based instance selection,.

Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection Noise-resistant deep metric learning with ranking-based instance selection,

Reference 4

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This paper cites K-means++ the advantages of careful seeding,.

Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection K-means++ the advantages of careful seeding,

Reference 5

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This paper cites Hierarchical clustering,.

Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection Hierarchical clustering,

Reference 6

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Observation 55a1cec0-9579-4f34-83fb-6f2ac7f06cdc · outbound

This paper cites Co-teaching: Robust training of deep neural networks with extremely noisy labels,.

Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection Co-teaching: Robust training of deep neural networks with extremely noisy labels,

Reference 7

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Observation 6c206718-8555-4958-bc34-d1dc2b5463ad · outbound

This paper cites Sample selection with uncertainty of losses for learning with noisy labels,.

Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection Sample selection with uncertainty of losses for learning with noisy labels,

Reference 8

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Observation c4aac351-b576-452c-9d00-241ec54735a6 · outbound

This paper cites Selective-supervised contrastive learning with noisy labels,.

Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection Selective-supervised contrastive learning with noisy labels,

Reference 9

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Observation d7c329c5-2337-48ab-adf3-d74c5b7b4864 · outbound

This paper cites Dist-pu: Positive- unlabeled learning from a label distribution perspective,.

Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection Dist-pu: Positive- unlabeled learning from a label distribution perspective,

Reference 10

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Observation 5dc48bea-ae81-4bee-9dcc-62b592e20ae3 · outbound

This paper cites Mentornet: Learning data-driven curriculum for very deep neural networks on corrupted labels,.

Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection Mentornet: Learning data-driven curriculum for very deep neural networks on corrupted labels,

Reference 11

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Observation ebc18e4d-4d53-4598-a492-37361fdf87fa · outbound

This paper cites Meta label correction for noisy label learning,.

Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection Meta label correction for noisy label learning,

Reference 12

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Observation 77307729-1a0e-4d97-b466-3b1c70efe3ea · outbound

This paper cites Making deep neural networks robust to label noise: A loss correction approach,.

Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection Making deep neural networks robust to label noise: A loss correction approach,

Reference 13

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Observation ec27d609-fa4a-4cd2-a4f6-f615cabc8ec7 · outbound

This paper cites Estimating noise transition matrix with label correlations for noisy multi-label learning,.

Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection Estimating noise transition matrix with label correlations for noisy multi-label learning,

Reference 14

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Observation 690d18c9-fad9-47bb-969c-703b23a38692 · outbound

This paper cites A parametrical model for instance-dependent label noise,.

Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection A parametrical model for instance-dependent label noise,

Reference 15

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Observation 64a1094a-db99-4bca-8e25-4d7dabbf2897 · outbound

This paper cites DivideMix: Learning with Noisy Labels as Semi-supervised Learning.

Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection DivideMix: Learning with Noisy Labels as Semi-supervised Learning

Reference 16

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Observation 92f72323-4f72-4767-b8b6-610963c760d6 · outbound

This paper cites Me-momentum: Extracting hard confident examples from noisily labeled data,.

Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection Me-momentum: Extracting hard confident examples from noisily labeled data,

Reference 17

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Observation afa435c5-e692-41a1-85ed-6bd5017911c4 · outbound

This paper cites Maxmatch: Semi-supervised learning with worst-case consistency,.

Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection Maxmatch: Semi-supervised learning with worst-case consistency,

Reference 18

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Observation dd920d03-2a76-4192-88d6-85924c6cb706 · outbound

This paper cites Learning a similarity metric discriminatively, with application to face verification,.

Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection Learning a similarity metric discriminatively, with application to face verification,

Reference 19

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Observation 18ade2d3-9b3f-4b4b-a5d5-256556633f53 · outbound

This paper cites Cross-batch memory for embedding learning,.

Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection Cross-batch memory for embedding learning,

Reference 20

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Observation d55a2e1b-2ebe-4361-8724-8f648ea85056 · outbound

This paper cites Deep image retrieval is not robust to label noise,.

Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection Deep image retrieval is not robust to label noise,

Reference 21

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Observation 17fdc3ae-545a-4538-ab0c-f7ce3399fe8a · outbound

This paper cites Facenet: A unified embed- ding for face recognition and clustering,.

Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection Facenet: A unified embed- ding for face recognition and clustering,

Reference 22

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Observation 06f59d0d-ef0f-4703-8236-c8a4cb506376 · outbound

This paper cites Circle loss: A unified perspective of pair similarity optimization,.

Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection Circle loss: A unified perspective of pair similarity optimization,

Reference 23

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This paper cites Attributable visual similarity learning,.

Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection Attributable visual similarity learning,

Reference 24

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Observation b54a0db9-0813-439b-af2d-64439805a2fd · outbound

This paper cites Neighbourhood components analysis,.

Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection Neighbourhood components analysis,

Reference 25

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This paper cites Sampling matters in deep embedding learning,.

Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection Sampling matters in deep embedding learning,

Reference 26

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Observation 168c8608-4ae6-4041-b223-eb0992e3db77 · outbound

This paper cites Deep metric learning to rank,.

Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection Deep metric learning to rank,

Reference 27

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Observation b33f1ca5-a5f6-4fd5-b04b-98d28696f67f · outbound

This paper cites Robust and decomposable average precision for image retrieval,.

Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection Robust and decomposable average precision for image retrieval,

Reference 28

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This paper cites Exploring the algorithm- dependent generalization of auprc optimization with list stability,.

Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection Exploring the algorithm- dependent generalization of auprc optimization with list stability,

Reference 29

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This paper cites Classification is a strong baseline for deep metric learning,.

Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection Classification is a strong baseline for deep metric learning,

Reference 30

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Observation 3b5ef8e4-0ac4-495c-80f3-b8fa85e8c084 · outbound

This paper cites No fuss distance metric learning using proxies,.

Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection No fuss distance metric learning using proxies,

Reference 31

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Observation b7cceb0c-e22e-46e4-b9cf-974125a388ea · outbound

This paper cites Softtriple loss: Deep metric learning without triplet sampling,.

Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection Softtriple loss: Deep metric learning without triplet sampling,

Reference 32

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Observation 62649483-235b-4695-8a81-b9b9c8d0354b · outbound

This paper cites Proxynca++: Revisiting and revitalizing proxy neighborhood component analysis,.

Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection Proxynca++: Revisiting and revitalizing proxy neighborhood component analysis,

Reference 33

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Observation 7fb6c16e-794c-410f-8032-6ec10d993da4 · outbound

This paper cites Unicom: Universal and Compact Representation Learning for Image Retrieval.

Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection Unicom: Universal and Compact Representation Learning for Image Retrieval

Reference 34

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Observation e9ae21a8-deee-470e-94c9-184aa0520fad · outbound

This paper cites Supervised metric learning to rank for retrieval via contextual similarity optimization,.

Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection Supervised metric learning to rank for retrieval via contextual similarity optimization,

Reference 35

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Observation 44800497-b2a4-47b7-82e3-7bd0a2f7bf2e · outbound

This paper cites Metricformer: A unified perspective of correlation exploring in similarity learning,.

Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection Metricformer: A unified perspective of correlation exploring in similarity learning,

Reference 36

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Observation f876fc59-ca04-4f22-a23f-f240018d0a75 · outbound

This paper cites Causality-invariant interactive mining for cross-modal similarity learning,.

Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection Causality-invariant interactive mining for cross-modal similarity learning,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:44:40.577823Z

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Observation c3c23ca0-7c54-4fde-ba5d-fc4e816a2df7 · outbound

This paper cites Learning from noisy examples,.

Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection Learning from noisy examples,

Reference 38

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Observation f3b1a7ba-1094-4de7-8f47-52b67962fc75 · outbound

This paper cites Iterative learning with open-set noisy labels,.

Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection Iterative learning with open-set noisy labels,

Reference 39

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Observation 81f6d60d-1cf6-410e-bfb9-bc9d96dac634 · outbound

This paper cites Which is better for learning with noisy labels: the semi-supervised method or modeling label noise?.

Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection Which is better for learning with noisy labels: the semi-supervised method or modeling label noise?

Reference 40

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Observation 6db21d5f-f926-4417-b44a-f9f6f4509a6c · outbound

This paper cites Psnea: Pseudo- siamese network for entity alignment between multi-modal knowledge graphs,.

Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection Psnea: Pseudo- siamese network for entity alignment between multi-modal knowledge graphs,

Reference 41

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Observation f2c5aabb-292a-4ea2-8f3e-ae3240b99c9f · outbound

This paper cites Positive-unlabeled learning with label distribution alignment,.

Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection Positive-unlabeled learning with label distribution alignment,

Reference 42

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Observation 3fff90da-e7d9-433c-bc99-3a98d95569a1 · outbound

This paper cites How does disagreement help generalization against label corruption?.

Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection How does disagreement help generalization against label corruption?

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:44:40.483429Z

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Observation 577cb23e-1540-4d5f-a342-d75d5f20ba2e · outbound

This paper cites Improving label noise robustness with data augmentation and semi-supervised learning (student abstract),.

Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection Improving label noise robustness with data augmentation and semi-supervised learning (student abstract),

Reference 44

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Observation 6783480a-bfea-492f-a330-e7e820be6a22 · outbound

This paper cites Regularized contrastive partial multi-view outlier detection,.

Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection Regularized contrastive partial multi-view outlier detection,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:44:40.452104Z

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Observation 447a337e-2cfc-463b-acb4-87c9bd44a3d0 · outbound

This paper cites Uni- con: Combating label noise through uniform selection and contrastive learning,.

Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection Uni- con: Combating label noise through uniform selection and contrastive learning,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:44:40.435441Z

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Observation d493aed8-cbf6-40b4-97c1-2a49f6801bcf · outbound

This paper cites Label-retrieval-augmented diffusion models for learning from noisy labels,.

Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection Label-retrieval-augmented diffusion models for learning from noisy labels,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:44:40.419588Z

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Observation e1c50774-5026-4f17-8548-9a569c344822 · outbound

This paper cites Understanding and improving early stopping for learning with noisy labels,.

Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection Understanding and improving early stopping for learning with noisy labels,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:44:40.402288Z

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Observation 7cd1473f-62ef-4f4f-a7a1-a3e14e5223aa · outbound

This paper cites Early stopping against label noise without validation data,.

Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection Early stopping against label noise without validation data,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:44:40.387090Z

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Observation fa1a1563-68b6-4212-8162-fcf7b2e734d0 · outbound

This paper cites Dm2c: Deep mixed- modal clustering,.

Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection Dm2c: Deep mixed- modal clustering,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:44:40.371381Z

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Observation ca3167a7-b988-4923-92ff-bcb136c363b5 · outbound

This paper cites When to learn what: Deep cognitive subspace clustering,.

Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection When to learn what: Deep cognitive subspace clustering,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:44:40.355542Z

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Observation e80d9049-bd57-4d78-9b6f-717b5239b901 · outbound

This paper cites Duet robust deep subspace clustering,.

Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection Duet robust deep subspace clustering,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:44:40.339885Z

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Observation 2655a934-8237-401f-a1b2-e9d5dc32af13 · outbound

This paper cites Robust distance metric learning via bayesian inference,.

Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection Robust distance metric learning via bayesian inference,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:44:40.324720Z

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Observation bbd266ea-5a3b-414d-bcba-328a309aac38 · outbound

This paper cites Deep metric learning by online soft mining and class-aware attention,.

Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection Deep metric learning by online soft mining and class-aware attention,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:44:40.309336Z

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Observation 58e15dbf-cad4-49b4-ab39-009f59fc10a5 · outbound

This paper cites Large-scale Landmark Retrieval/Recognition under a Noisy and Diverse Dataset.

Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection Large-scale Landmark Retrieval/Recognition under a Noisy and Diverse Dataset

Reference 55

Resolution: verified exact; local_arxiv, observed 2026-08-10T18:44:39.738029Z

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Observation 8760ceb3-4df9-4273-9cac-4eaaa6fe9ae8 · outbound

This paper cites Hyperbolic vision transformers: Combining improvements in metric learning,.

Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection Hyperbolic vision transformers: Combining improvements in metric learning,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:44:40.293008Z

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Observation 2363922c-c2e2-4977-8773-28126ff08962 · outbound

This paper cites Adaptive hierarchical similarity metric learning with noisy labels,.

Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection Adaptive hierarchical similarity metric learning with noisy labels,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:44:40.278031Z

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Observation 4488bea5-4d46-4cc2-8884-fd6e5ace62fa · outbound

This paper cites Unsupervised hyperbolic metric learning,.

Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection Unsupervised hyperbolic metric learning,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:44:40.262850Z

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Observation 35fc11c8-f48a-450f-b73f-ca1beaed84e5 · outbound

This paper cites One for more: Selecting generalizable samples for generalizable reid model,.

Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection One for more: Selecting generalizable samples for generalizable reid model,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:44:40.246899Z

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Observation ceaff385-7ea5-4a21-9317-0beb4b481aef · outbound

This paper cites Collaborative refining for person re-identification with label noise,.

Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection Collaborative refining for person re-identification with label noise,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:44:40.230975Z

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Observation 132a007e-eeb9-4a01-be59-b6883fb99089 · outbound

This paper cites Noise is also useful: Negative correlation-steered latent contrastive learning,.

Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection Noise is also useful: Negative correlation-steered latent contrastive learning,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:44:40.212032Z

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Observation a30f1731-01fd-464d-99ef-37c55dc4101d · outbound

This paper cites Learning to purifi- cation for unsupervised person re-identification,.

Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection Learning to purifi- cation for unsupervised person re-identification,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:44:40.196038Z

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Observation 9522f7f9-0c2f-44ad-9639-1ef4d637ebb1 · outbound

This paper cites Momentum contrast for unsupervised visual representation learning,.

Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection Momentum contrast for unsupervised visual representation learning,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:44:40.179680Z

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Observation 69e671de-5c19-47e7-9fb5-f660ec939750 · outbound

This paper cites Online deep clustering for unsupervised representation learning,.

Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection Online deep clustering for unsupervised representation learning,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:44:40.163212Z

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Observation cc0acfdc-cd4a-46a8-8a73-f155ab1ce83f · outbound

This paper cites Percolation and cluster distribution. i. cluster multiple labeling technique and critical concentration algorithm,.

Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection Percolation and cluster distribution. i. cluster multiple labeling technique and critical concentration algorithm,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:44:40.145632Z

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Observation c648bac2-90d4-45f7-aac1-834f87ccf3c6 · outbound

This paper cites Hierarchical clustering schemes,.

Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection Hierarchical clustering schemes,

Reference 66

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Observation 81f152a3-a455-489f-8f91-578a2bab8e05 · outbound

This paper cites Mean shift: A robust approach toward feature space analysis,.

Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection Mean shift: A robust approach toward feature space analysis,

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:44:40.118737Z

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Observation b4291a98-39da-4c6b-82e5-f19053eb2f35 · outbound

This paper cites 3d object representations for fine-grained categorization,.

Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection 3d object representations for fine-grained categorization,

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:44:40.102105Z

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Observation 1c343c1a-a9be-4132-9a74-a9661a369a2e · outbound

This paper cites The caltech-ucsd birds-200-2011 dataset,.

Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection The caltech-ucsd birds-200-2011 dataset,

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-10T18:44:39.598227Z

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This paper cites Deep metric learning via lifted structured feature embedding,.

Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection Deep metric learning via lifted structured feature embedding,

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:44:40.075314Z

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Observation ecdcb92a-1ed4-49f2-9073-d121658abcad · outbound

This paper cites Cleannet: Transfer learning for scalable image classifier training with label noise,.

Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection Cleannet: Transfer learning for scalable image classifier training with label noise,

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:44:40.059768Z

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Observation e2d4179b-504c-43fc-854c-5075db00da95 · outbound

This paper cites Food-101–mining discriminative components with random forests,.

Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection Food-101–mining discriminative components with random forests,

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-10T18:44:39.612805Z

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Observation ba6b4d92-c3d8-40af-987a-02be5d06911f · outbound

This paper cites Ms-celeb-1m: A dataset and benchmark for large-scale face recognition,.

Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection Ms-celeb-1m: A dataset and benchmark for large-scale face recognition,

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:44:40.031098Z

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Observation 32c00107-191b-47af-8e36-5b252a954c8b · outbound

This paper cites Learning from massive noisy labeled data for image classification,.

Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection Learning from massive noisy labeled data for image classification,

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:44:40.013424Z

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Observation cfb76d21-afc1-4ffc-8b49-6cab6449fb37 · outbound

This paper cites Learning with symmetric label noise: The importance of being unhinged,.

Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection Learning with symmetric label noise: The importance of being unhinged,

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:44:39.996751Z

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Observation b33272ed-1a58-49f0-ab5d-261daf0aed84 · outbound

This paper cites Supervised contrastive learn- ing,.

Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection Supervised contrastive learn- ing,

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:44:39.979338Z

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Observation 97fd85b7-b600-4b4b-a3dd-2667e1c408a7 · outbound

This paper cites Batch normalization: Accelerating deep network training by reducing internal covariate shift,.

Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection Batch normalization: Accelerating deep network training by reducing internal covariate shift,

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:44:39.963262Z

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Observation 9946747a-1ccf-435d-8717-56162decbe3d · outbound

This paper cites A metric learning reality check,.

Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection A metric learning reality check,

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:44:39.946766Z

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Observation c7884dce-69e5-4f2a-b848-b246fea5ffcf · outbound

This paper cites Paddlepaddle: An open-source deep learning platform from industrial practice,.

Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection Paddlepaddle: An open-source deep learning platform from industrial practice,

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-10T18:44:39.646805Z

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Observation 5d449f3c-2d85-4bf9-9b28-ba4351f4af46 · outbound

This paper cites Dynamic class queue for large scale face recognition in the wild,.

Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection Dynamic class queue for large scale face recognition in the wild,

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:44:39.919922Z

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Observation 9d9cedf3-c97e-420c-b2b4-2136d959945c · outbound

This paper cites Cosface: Large margin cosine loss for deep face recognition,.

Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection Cosface: Large margin cosine loss for deep face recognition,

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-10T18:44:39.656516Z

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Observation 5d4057f1-4652-4f00-b9a0-c39dbd4c123a · outbound

This paper cites Noise-tolerant paradigm for training face recognition cnns,.

Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection Noise-tolerant paradigm for training face recognition cnns,

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:44:39.892924Z

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Observation ce6588a9-beb9-439c-ae81-9f5a07cbef22 · outbound

This paper cites Unequal-training for deep face recognition with long-tailed noisy data,.

Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection Unequal-training for deep face recognition with long-tailed noisy data,

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:44:39.876229Z

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Observation 0d6875fd-8df8-45ed-9aac-c51dce9a5ac6 · outbound

This paper cites Co-mining: Deep face recognition with noisy labels,.

Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection Co-mining: Deep face recognition with noisy labels,

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:44:39.858003Z

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Observation 6c3006ee-d427-450f-9a97-c10935cc9442 · outbound

This paper cites Sub-center arcface: Boosting face recognition by large-scale noisy web faces,.

Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection Sub-center arcface: Boosting face recognition by large-scale noisy web faces,

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:44:39.840709Z

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Observation a648600b-1b87-414d-9302-e949e7f4d9c2 · outbound

This paper cites Switchable k-class hyperplanes for noise-robust representation learning,.

Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection Switchable k-class hyperplanes for noise-robust representation learning,

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:44:39.824752Z

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Observation b94d5b0d-b51c-4c44-98cb-73a3d72761ab · outbound

This paper cites An efficient training approach for very large scale face recognition,.

Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection An efficient training approach for very large scale face recognition,

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:44:39.808350Z

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Observation cd7169dc-5d21-4259-9d38-454c094fa004 · outbound

This paper cites Her research interests include machine learning and computer vision.

Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection Her research interests include machine learning and computer vision

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:44:39.792112Z

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