GIF: Generative Inspiration for Face Recognition at Scale | Paper Citation Record

Observation 5ea7f1a7-eaf2-408f-9788-b34c0fcc3cb2 · outbound

This paper cites Partial fc: Training 10 million identities on a single ma- chine.

GIF: Generative Inspiration for Face Recognition at Scale Partial fc: Training 10 million identities on a single ma- chine

Reference 1

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Observation 41855349-a84d-4380-be97-2e7c0d507352 · outbound

This paper cites Killing two birds with one stone: Efficient and robust training of face recognition cnns by partial fc.

GIF: Generative Inspiration for Face Recognition at Scale Killing two birds with one stone: Efficient and robust training of face recognition cnns by partial fc

Reference 2

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Observation 7a393070-1ef3-478e-a1d2-9fcfe92cc9df · outbound

This paper cites Umdfaces: An annotated face dataset for training deep networks.

GIF: Generative Inspiration for Face Recognition at Scale Umdfaces: An annotated face dataset for training deep networks

Reference 3

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Observation 9f2c95b9-a919-4488-8acd-3c05056f89b4 · outbound

This paper cites Monotone funktionen, stieltjessche in- tegrale und harmonische analyse.

GIF: Generative Inspiration for Face Recognition at Scale Monotone funktionen, stieltjessche in- tegrale und harmonische analyse

Reference 4

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Observation 0caa3576-862f-4557-ab10-100655bff86c · outbound

This paper cites Vggface2: A dataset for recognising faces across pose and age.

GIF: Generative Inspiration for Face Recognition at Scale Vggface2: A dataset for recognising faces across pose and age

Reference 5

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Observation 9ad855a1-504a-4065-907e-6ed333a6d263 · outbound

This paper cites Unsupervised learning of visual features by contrasting cluster assignments.

GIF: Generative Inspiration for Face Recognition at Scale Unsupervised learning of visual features by contrasting cluster assignments

Reference 6

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Observation 442ba4c1-ed23-48e9-8cc0-f9faedf72420 · outbound

This paper cites Emerg- ing properties in self-supervised vision transformers.

GIF: Generative Inspiration for Face Recognition at Scale Emerg- ing properties in self-supervised vision transformers

Reference 7

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Observation a3a440c4-07dd-4f13-befb-bd0de30195b3 · outbound

This paper cites A generative approach for wikipedia-scale visual entity recognition.

GIF: Generative Inspiration for Face Recognition at Scale A generative approach for wikipedia-scale visual entity recognition

Reference 8

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Observation f3809407-8ed0-4d4c-bb56-77e5c686ef6b · outbound

This paper cites Data uncertainty learning in face recognition.

GIF: Generative Inspiration for Face Recognition at Scale Data uncertainty learning in face recognition

Reference 9

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Observation 879a5673-efca-41c2-85f0-6bd345c81b52 · outbound

This paper cites Autoregressive Entity Retrieval.

GIF: Generative Inspiration for Face Recognition at Scale Autoregressive Entity Retrieval

Reference 10

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Observation b7e5e563-3e03-408d-bfc6-f584c37f913b · outbound

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

GIF: Generative Inspiration for Face Recognition at Scale Arcface: Additive angular margin loss for deep face recognition

Reference 11

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Observation 49e52a5c-c314-48d4-8315-70fd11d710a1 · outbound

This paper cites Variational prototype learning for deep face recognition.

GIF: Generative Inspiration for Face Recognition at Scale Variational prototype learning for deep face recognition

Reference 12

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Observation 7a5e7070-d936-4c38-80b2-b61a65263cb1 · outbound

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

GIF: Generative Inspiration for Face Recognition at Scale An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 13

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Observation b3f0c986-5070-4864-8575-f7cce17cb3eb · outbound

This paper cites Uniformface: Learn- ing deep equidistributed representation for face recognition.

GIF: Generative Inspiration for Face Recognition at Scale Uniformface: Learn- ing deep equidistributed representation for face recognition

Reference 14

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Observation 0e0752e8-daee-4e14-975e-3c7145c9f99a · outbound

This paper cites Explor- ing deep neural networks via layer-peeled model: Minority collapse in imbalanced training.

GIF: Generative Inspiration for Face Recognition at Scale Explor- ing deep neural networks via layer-peeled model: Minority collapse in imbalanced training

Reference 15

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Observation be270cf2-5d3e-45f8-94cc-9d91a2c1bc29 · outbound

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

GIF: Generative Inspiration for Face Recognition at Scale Ms-celeb-1m: A dataset and benchmark for large-scale face recognition

Reference 16

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Observation 0940c763-6bdc-4378-8f26-9209ff1dd66b · outbound

This paper cites Deep residual learning for image recognition.

GIF: Generative Inspiration for Face Recognition at Scale Deep residual learning for image recognition

Reference 17

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Observation d166952b-47a7-4da6-8f62-d50f315bc082 · outbound

This paper cites Momentum contrast for unsupervised visual rep- resentation learning.

GIF: Generative Inspiration for Face Recognition at Scale Momentum contrast for unsupervised visual rep- resentation learning

Reference 18

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Observation e968c20f-1f5f-432f-b16b-e1069c93b31b · outbound

This paper cites Fix your classifier: the marginal value of training the last weight layer.

GIF: Generative Inspiration for Face Recognition at Scale Fix your classifier: the marginal value of training the last weight layer

Reference 19

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Observation f97a24b9-3093-4545-aaf2-6793cb15da9a · outbound

This paper cites Open-domain visual entity recognition: Towards recognizing millions of wikipedia entities.

GIF: Generative Inspiration for Face Recognition at Scale Open-domain visual entity recognition: Towards recognizing millions of wikipedia entities

Reference 20

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Observation 0ba15ed2-ed20-4b5a-9bf0-67895b567f4c · outbound

This paper cites Labeled faces in the wild: A database forstudying face recognition in unconstrained environments.

GIF: Generative Inspiration for Face Recognition at Scale Labeled faces in the wild: A database forstudying face recognition in unconstrained environments

Reference 21

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Observation 14cdaa17-ee6c-4209-a812-b418abc64b2b · outbound

This paper cites Curricularface: adaptive curriculum learning loss for deep face recognition.

GIF: Generative Inspiration for Face Recognition at Scale Curricularface: adaptive curriculum learning loss for deep face recognition

Reference 22

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Observation 5e06254a-5e23-43ca-bcbe-1e93694d346c · outbound

This paper cites Max- imum class separation as inductive bias in one matrix.

GIF: Generative Inspiration for Face Recognition at Scale Max- imum class separation as inductive bias in one matrix

Reference 23

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Observation 2a36e607-fd21-4ca5-b479-af2f253c7380 · outbound

This paper cites Introduction to pytorch.

GIF: Generative Inspiration for Face Recognition at Scale Introduction to pytorch

Reference 24

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Observation d649dc6b-119e-46fc-b51d-afbeee8aa31f · outbound

This paper cites Supervised contrastive learning.

GIF: Generative Inspiration for Face Recognition at Scale Supervised contrastive learning

Reference 25

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Observation 4ae8b65f-d51f-4ed7-b6f4-fc6a3068398e · outbound

This paper cites Adaface: Quality adaptive margin for face recognition.

GIF: Generative Inspiration for Face Recognition at Scale Adaface: Quality adaptive margin for face recognition

Reference 26

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Observation 41dcc98f-735b-49b3-805a-85a323d84660 · outbound

This paper cites Groupface: Learning latent groups and con- structing group-based representations for face recognition.

GIF: Generative Inspiration for Face Recognition at Scale Groupface: Learning latent groups and con- structing group-based representations for face recognition

Reference 27

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Observation 9c870988-3738-413d-9eaa-4951af91d7c2 · outbound

This paper cites Broad- face: Looking at tens of thousands of people at once for face recognition.

GIF: Generative Inspiration for Face Recognition at Scale Broad- face: Looking at tens of thousands of people at once for face recognition

Reference 28

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Observation 43bcfe69-9b99-4d1b-9634-4cd65967ebec · outbound

This paper cites Recurrent pixel embed- ding for instance grouping.

GIF: Generative Inspiration for Face Recognition at Scale Recurrent pixel embed- ding for instance grouping

Reference 29

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Observation 9fa93a8f-5492-4c9c-b4e3-115e7e773376 · outbound

This paper cites SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing.

GIF: Generative Inspiration for Face Recognition at Scale SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing

Reference 30

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Observation bf197203-a15e-4e06-bedd-0cfbf35ee4fd · outbound

This paper cites Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidates.

GIF: Generative Inspiration for Face Recognition at Scale Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidates

Reference 31

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Observation e5de625c-adbd-4dff-ab88-e7e45323e939 · outbound

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

GIF: Generative Inspiration for Face Recognition at Scale Dynamic class queue for large scale face recognition in the wild

Reference 32

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Observation f6aa4f6a-5608-4da4-9fed-80a59a1a058c · outbound

This paper cites Virtual fully- connected layer: Training a large-scale face recognition dataset with limited computational resources.

GIF: Generative Inspiration for Face Recognition at Scale Virtual fully- connected layer: Training a large-scale face recognition dataset with limited computational resources

Reference 33

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Observation 59ece6f3-48fe-4491-9a0d-4e76cc2b5f48 · outbound

This paper cites Con- trollable and guided face synthesis for unconstrained face recognition.

GIF: Generative Inspiration for Face Recognition at Scale Con- trollable and guided face synthesis for unconstrained face recognition

Reference 34

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Observation 5a66dd97-a70c-41d7-912a-710318e31f5b · outbound

This paper cites Sphereface: Deep hypersphere embedding for face recognition.

GIF: Generative Inspiration for Face Recognition at Scale Sphereface: Deep hypersphere embedding for face recognition

Reference 35

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Observation 7c0fa634-9f9a-498a-97de-816ef2f9af13 · outbound

This paper cites Cosine normalization: Using cosine similarity instead of dot product in neural networks.

GIF: Generative Inspiration for Face Recognition at Scale Cosine normalization: Using cosine similarity instead of dot product in neural networks

Reference 36

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Observation 63830966-c5a1-4ed0-8952-d44e22b7c106 · outbound

This paper cites Iarpa janus benchmark-c: Face dataset and protocol.

GIF: Generative Inspiration for Face Recognition at Scale Iarpa janus benchmark-c: Face dataset and protocol

Reference 37

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Observation 0df6a938-7337-42e2-be64-b54a04af42fb · outbound

This paper cites Magface: A universal representation for face recognition and quality assessment.

GIF: Generative Inspiration for Face Recognition at Scale Magface: A universal representation for face recognition and quality assessment

Reference 38

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Observation b63460f2-52b4-4e25-bd26-a31db898b3ce · outbound

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

GIF: Generative Inspiration for Face Recognition at Scale Distance-based image classification: Gen- eralizing to new classes at near-zero cost

Reference 39

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Observation c7abf782-3b4a-408e-8a7f-ac88fc1e8e50 · outbound

This paper cites Hyper- spherical prototype networks.

GIF: Generative Inspiration for Face Recognition at Scale Hyper- spherical prototype networks

Reference 40

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Observation e3015063-45da-4bdb-bcf8-2ee8cb989827 · outbound

This paper cites Distributed representations of words and phrases and their compositionality.

GIF: Generative Inspiration for Face Recognition at Scale Distributed representations of words and phrases and their compositionality

Reference 41

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Observation 589dcb28-eb7c-4e38-b2c5-3df256355a50 · outbound

This paper cites Agedb: the first manually collected, in-the-wild age database.

GIF: Generative Inspiration for Face Recognition at Scale Agedb: the first manually collected, in-the-wild age database

Reference 42

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Observation e389470d-81ab-4739-ad48-9a160c5a1a18 · outbound

This paper cites No fuss distance met- ric learning using proxies.

GIF: Generative Inspiration for Face Recognition at Scale No fuss distance met- ric learning using proxies

Reference 43

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Observation bcb8086d-3192-41e5-925f-736640ae37e5 · outbound

This paper cites Level play- ing field for million scale face recognition.

GIF: Generative Inspiration for Face Recognition at Scale Level play- ing field for million scale face recognition

Reference 44

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Observation 60f7769c-994b-4696-8b21-e1518136ea49 · outbound

This paper cites Deep face recognition.

GIF: Generative Inspiration for Face Recognition at Scale Deep face recognition

Reference 45

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This paper cites Synface: Face recognition with syn- thetic data.

GIF: Generative Inspiration for Face Recognition at Scale Synface: Face recognition with syn- thetic data

Reference 46

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Observation d858b036-4e3a-4f7f-ad13-57e35cd250ff · outbound

This paper cites Learn- ing transferable visual models from natural language super- vision.

GIF: Generative Inspiration for Face Recognition at Scale Learn- ing transferable visual models from natural language super- vision

Reference 47

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Observation d49034bc-076f-4713-85b6-cc03c21b558f · outbound

This paper cites Recommender Systems with Generative Retrieval.

GIF: Generative Inspiration for Face Recognition at Scale Recommender Systems with Generative Retrieval

Reference 48

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Observation 589a9051-27d0-4b32-8cc2-b4f9a709ea79 · outbound

This paper cites Deep convolutional neu- ral networks for image classification: A comprehensive re- view.

GIF: Generative Inspiration for Face Recognition at Scale Deep convolutional neu- ral networks for image classification: A comprehensive re- view

Reference 49

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Observation f3ab9107-382a-4907-97ee-6523f2119141 · outbound

This paper cites A quality aware sample-to-sample comparison for face recognition.

GIF: Generative Inspiration for Face Recognition at Scale A quality aware sample-to-sample comparison for face recognition

Reference 50

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Observation 52c1fabb-5d19-4c0a-9dfe-b5c875924687 · outbound

This paper cites Distributing many points on a sphere.

GIF: Generative Inspiration for Face Recognition at Scale Distributing many points on a sphere

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:08:21.242271Z

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Observation 567b7487-99fb-4d91-8d0c-b409a200b30b · outbound

This paper cites Facenet: A unified embedding for face recognition and clus- tering.

GIF: Generative Inspiration for Face Recognition at Scale Facenet: A unified embedding for face recognition and clus- tering

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:08:21.227968Z

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Observation 8b156101-3dda-4f20-afd9-2ed947ca197a · outbound

This paper cites Frontal to profile face verification in the wild.

GIF: Generative Inspiration for Face Recognition at Scale Frontal to profile face verification in the wild

Reference 53

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Observation 450b6dcf-8548-4de4-af88-7fcdddf95922 · outbound

This paper cites Neural Machine Translation of Rare Words with Subword Units.

GIF: Generative Inspiration for Face Recognition at Scale Neural Machine Translation of Rare Words with Subword Units

Reference 54

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Observation 36ce69ce-be24-49e5-90a6-65040d4382f6 · outbound

This paper cites Equiangular basis vectors.

GIF: Generative Inspiration for Face Recognition at Scale Equiangular basis vectors

Reference 55

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Observation 7ad7f48c-a080-4bd6-b3dc-2676391d915f · outbound

This paper cites Multi- attention multi-class constraint for fine-grained image recog- nition.

GIF: Generative Inspiration for Face Recognition at Scale Multi- attention multi-class constraint for fine-grained image recog- nition

Reference 56

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Observation b70c2450-6ce8-45b7-a719-473f1d2aa3d3 · outbound

This paper cites On the origin of num- ber and arrangement of the places of exit on the surface of pollen-grains.

GIF: Generative Inspiration for Face Recognition at Scale On the origin of num- ber and arrangement of the places of exit on the surface of pollen-grains

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:08:21.182434Z

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Observation b00b38bc-a642-45e8-81cb-e89dae96a222 · outbound

This paper cites Transformer memory as a differentiable search index.

GIF: Generative Inspiration for Face Recognition at Scale Transformer memory as a differentiable search index

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:08:21.169746Z

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This paper cites Normface: L2 hypersphere embedding for face veri- fication.

GIF: Generative Inspiration for Face Recognition at Scale Normface: L2 hypersphere embedding for face veri- fication

Reference 59

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Observation ec44ae1e-1454-44ca-853d-b48e984db665 · outbound

This paper cites The devil of face recognition is in the noise.

GIF: Generative Inspiration for Face Recognition at Scale The devil of face recognition is in the noise

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:08:21.143524Z

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Observation 901578f1-670f-459c-89f1-27a06cbd7db9 · outbound

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

GIF: Generative Inspiration for Face Recognition at Scale CosFace: Large margin cosine loss for deep face recognition

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:08:21.130953Z

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Observation 67d32bee-8c23-41c7-8b56-538521a89d9a · outbound

This paper cites GIT: A Generative Image-to-text Transformer for Vision and Language.

GIF: Generative Inspiration for Face Recognition at Scale GIT: A Generative Image-to-text Transformer for Vision and Language

Reference 62

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

GIF: Generative Inspiration for Face Recognition at Scale An efficient training approach for very large scale face recognition

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:08:21.117361Z

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Observation 8b643f41-976c-4afc-a4a7-38dbcda9642b · outbound

This paper cites Understanding contrastive representation learning through alignment and uniformity on the hypersphere.

GIF: Generative Inspiration for Face Recognition at Scale Understanding contrastive representation learning through alignment and uniformity on the hypersphere

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:08:21.102648Z

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Observation 0fb392cb-3cd5-4120-960f-321580f128a7 · outbound

This paper cites Visual Recognition with Deep Nearest Centroids.

GIF: Generative Inspiration for Face Recognition at Scale Visual Recognition with Deep Nearest Centroids

Reference 65

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This paper cites SphereFace2: Binary Classification is All You Need for Deep Face Recognition.

GIF: Generative Inspiration for Face Recognition at Scale SphereFace2: Binary Classification is All You Need for Deep Face Recognition

Reference 66

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Observation a4466ad3-8a67-48d1-a4c6-775e17c88ded · outbound

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GIF: Generative Inspiration for Face Recognition at Scale Iarpa janus benchmark-b face dataset

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:08:21.088523Z

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Observation e045c654-d2d0-49d4-9b05-dcc877208177 · outbound

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GIF: Generative Inspiration for Face Recognition at Scale Unresolved cited work

Reference 68

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Observation c1806ac4-b198-430a-9540-4019068ea4b3 · outbound

This paper cites Neural collapse with normalized features: A ge- ometric analysis over the riemannian manifold.

GIF: Generative Inspiration for Face Recognition at Scale Neural collapse with normalized features: A ge- ometric analysis over the riemannian manifold

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:08:21.063242Z

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Observation 4ca3ba02-3db5-4b17-ac20-f627f7550a84 · outbound

This paper cites Learning Face Representation from Scratch.

GIF: Generative Inspiration for Face Recognition at Scale Learning Face Representation from Scratch

Reference 70

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Observation 2a00dcce-da2d-4b25-a351-743e8d54610c · outbound

This paper cites Distribution alignment: A unified frame- work for long-tail visual recognition.

GIF: Generative Inspiration for Face Recognition at Scale Distribution alignment: A unified frame- work for long-tail visual recognition

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:08:21.050463Z

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Observation 56c517e4-15fe-4d84-935c-697bd51ea4d4 · outbound

This paper cites Accelerated training for massive classification via dynamic class selection.

GIF: Generative Inspiration for Face Recognition at Scale Accelerated training for massive classification via dynamic class selection

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:08:21.034605Z

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Observation 475c3be2-551c-400c-8e9f-f5aa917a8777 · outbound

This paper cites Adacos: Adaptively scaling cosine logits for effec- tively learning deep face representations.

GIF: Generative Inspiration for Face Recognition at Scale Adacos: Adaptively scaling cosine logits for effec- tively learning deep face representations

Reference 73

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Observation 32250e79-07fa-43b0-a1f5-f7a1724699d1 · outbound

This paper cites Cross-pose lfw: A database for studying cross-pose face recognition in un- constrained environments.

GIF: Generative Inspiration for Face Recognition at Scale Cross-pose lfw: A database for studying cross-pose face recognition in un- constrained environments

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:08:21.009625Z

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Observation 0f9c4142-7c26-42c5-870e-4c96ac27d3af · outbound

This paper cites Cross-Age LFW: A Database for Studying Cross-Age Face Recognition in Unconstrained Environments.

GIF: Generative Inspiration for Face Recognition at Scale Cross-Age LFW: A Database for Studying Cross-Age Face Recognition in Unconstrained Environments

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-16T00:08:20.640085Z

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Observation c5aea527-403b-4716-91b6-24f77bf4e664 · outbound

This paper cites WebFace260M: A benchmark unveiling the power of million-scale deep face recognition.

GIF: Generative Inspiration for Face Recognition at Scale WebFace260M: A benchmark unveiling the power of million-scale deep face recognition

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:08:20.990582Z

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Observation 39be7855-d7ec-45dc-9cdb-d9adb568b55b · outbound

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GIF: Generative Inspiration for Face Recognition at Scale Unresolved cited work

Reference 78

Resolution: unresolved; raw_fallback, observed 2026-08-16T00:08:20.963525Z

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Observation 5d4a412f-7145-4e48-8cc8-9f1a7c1c6708 · outbound

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GIF: Generative Inspiration for Face Recognition at Scale Unresolved cited work

Reference 79

Resolution: unresolved; raw_fallback, observed 2026-08-16T00:08:20.950849Z

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Observation 634be53c-ee3e-4718-9107-4b70e1737e12 · outbound

This paper cites As demonstrated in Figure 7a, even with the number of identities reaching 64 million, the GPU memory usage remains significantly lower than the OOM threshold.

GIF: Generative Inspiration for Face Recognition at Scale As demonstrated in Figure 7a, even with the number of identities reaching 64 million, the GPU memory usage remains significantly lower than the OOM threshold

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:08:20.936601Z

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Observation 163a4ea1-8d09-4538-ba74-c316cdf4f6ec · outbound

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GIF: Generative Inspiration for Face Recognition at Scale Unresolved cited work

Reference 81

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Observation 372e2d15-fb0b-4fab-8e2a-113ce2192bfe · outbound

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GIF: Generative Inspiration for Face Recognition at Scale Unresolved cited work

Reference 2021

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