https://github.com/google-research/google-research/tree/master/class_balanced_distillation
공식 코드Class-Balanced Distillation for Long-Tailed Visual Recognition
Papers with Code 아카이브
Long-tail Learning
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Long-tailed learning, one of the most challenging problems in visual recognition, aims to train well-performing models from a large number of images that follow a long-tailed class distribution.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/google-research/google-research/tree/master/logit_adjustment
공식 코드https://github.com/google-research/google-research/tree/master/muNet
공식 코드A Continual Development Methodology for Large-scale Multitask Dynamic ML Systems
https://github.com/hitachi-rd-cv/CDB-loss
공식 코드Class-Wise Difficulty-Balanced Loss for Solving Class-Imbalance
https://github.com/hitachi-rd-cv/Difficulty_Net
공식 코드Difficulty-Net: Learning to Predict Difficulty for Long-Tailed Recognition
https://github.com/hyperconnect/LADE
공식 코드Disentangling Label Distribution for Long-tailed Visual Recognition
https://github.com/jayanthkoushik/alphanet
공식 코드AlphaNet: Improving Long-Tail Classification By Combining Classifiers
https://github.com/jianhongbai/colt
공식 코드On the Effectiveness of Out-of-Distribution Data in Self-Supervised Long-Tail Learning
https://github.com/jiawei-ren/BalancedMetaSoftmax
공식 코드https://github.com/jiequancui/Parametric-Contrastive-Learning
공식 코드https://github.com/jiequancui/ResLT
공식 코드https://github.com/juyongjiang/fbl
공식 코드https://github.com/kaidic/LDAM-DRW
공식 코드Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss
https://github.com/kaihuatang/generalized-long-tailed-benchmarks.pytorch
공식 코드Invariant Feature Learning for Generalized Long-Tailed Classification
https://github.com/keke921/gclloss
공식 코드Long-tailed Visual Recognition via Gaussian Clouded Logit Adjustment
https://github.com/keke921/gnm-pt
공식 코드https://github.com/kostas1515/aglu
공식 코드https://github.com/lblaoke/tlc
공식 코드https://github.com/leaplabthu/proco
공식 코드Probabilistic Contrastive Learning for Long-Tailed Visual Recognition
https://github.com/lth14/targeted-supcon
공식 코드Targeted Supervised Contrastive Learning for Long-Tailed Recognition
https://github.com/lumia-group/lot-insts
공식 코드Text Classification in the Wild: a Large-scale Long-tailed Name Normalization Dataset
https://github.com/mayanbiao1234/geometric-metrics-for-perceptual-manifolds
공식 코드Predicting and Enhancing the Fairness of DNNs with the Curvature of Perceptual Manifolds
https://github.com/mayanbiao1234/geometric-metrics-for-perceptual-manifolds-in-deep-neural-networks
공식 코드Predicting and Enhancing the Fairness of DNNs with the Curvature of Perceptual Manifolds
https://github.com/mcg-nju/ssd-lt
공식 코드Self Supervision to Distillation for Long-Tailed Visual Recognition
https://github.com/naver-ai/cmo
공식 코드https://github.com/openai/CLIP
공식 코드Learning Transferable Visual Models From Natural Language Supervision
https://github.com/pseulki/ib-loss
공식 코드Influence-Balanced Loss for Imbalanced Visual Classification
https://github.com/rahulvigneswaran/tailcalibx
공식 코드https://github.com/richard-peng-xia/LMPT
공식 코드https://github.com/richardaecn/class-balanced-loss
공식 코드https://github.com/saurabhsharma1993/prototype-classifier-ltr
공식 코드https://github.com/scongl/dirmixe
공식 코드Harnessing Hierarchical Label Distribution Variations in Test Agnostic Long-tail Recognition
https://github.com/shadealsha/ltr-weight-balancing
공식 코드https://github.com/shangxinyi/fedic
공식 코드FEDIC: Federated Learning on Non-IID and Long-Tailed Data via Calibrated Distillation
https://github.com/shijxcs/lift
공식 코드Long-Tail Learning with Foundation Model: Heavy Fine-Tuning Hurts
https://github.com/shijxcs/lift-plus
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.