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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/ssfootball04/class-balanced-experts
공식 코드https://github.com/stomach-ache/long-tailed-ood-detection
공식 코드https://github.com/sumyeongahn/cuda_ltr
공식 코드CUDA: Curriculum of Data Augmentation for Long-Tailed Recognition
https://github.com/tkasarla/max-separation-as-inductive-bias
공식 코드https://github.com/tztztztztz/eql.detectron2
공식 코드https://github.com/val-iisc/DeiT-LT
공식 코드DeiT-LT Distillation Strikes Back for Vision Transformer Training on Long-Tailed Datasets
https://github.com/val-iisc/saddle-longtail
공식 코드Escaping Saddle Points for Effective Generalization on Class-Imbalanced Data
https://github.com/vanint/sade-agnosticlt
공식 코드Self-Supervised Aggregation of Diverse Experts for Test-Agnostic Long-Tailed Recognition
https://github.com/vita-group/longtailcxr
공식 코드Long-Tailed Classification of Thorax Diseases on Chest X-Ray: A New Benchmark Study
https://github.com/wutong16/DistributionBalancedLoss
공식 코드Distribution-Balanced Loss for Multi-Label Classification in Long-Tailed Datasets
https://github.com/xiangly55/LFME
공식 코드Learning From Multiple Experts: Self-paced Knowledge Distillation for Long-tailed Classification
https://github.com/xiaohua-chen/risda
공식 코드https://github.com/xuzhengzhuo/livt
공식 코드https://github.com/ynu-yangpeng/GLMC
공식 코드Global and Local Mixture Consistency Cumulative Learning for Long-tailed Visual Recognitions
https://github.com/zhihou7/batchformer
공식 코드BatchFormer: Learning to Explore Sample Relationships for Robust Representation Learning
https://github.com/zhouzihao11/ccl
공식 코드Continuous Contrastive Learning for Long-Tailed Semi-Supervised Recognition
https://github.com/2023-MindSpore-1/ms-code-216/tree/main/retinanet_resnet101
https://github.com/2023-MindSpore-1/ms-code-216/tree/main/retinanet_resnet152
https://github.com/2023-MindSpore-1/ms-code-223/tree/main/RetinaNet
https://github.com/2023-MindSpore-1/ms-code-44
https://github.com/2023-MindSpore-4/Code10/tree/main/RetinaNet
https://github.com/2023-MindSpore-4/Code11/tree/main/retinanet_resnet101
https://github.com/2023-MindSpore-4/Code12/tree/main/MindFormers/clip
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/2023-MindSpore-4/Code13/tree/main/zhangchangqing/TMC_MS-main
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/clip
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/2024-MindSpore-1/Code4/tree/main/RetinaFace_ResNet50
https://github.com/2024-MindSpore-1/Code4/tree/main/RetinaNet
https://github.com/497626895/darknet
https://github.com/AdeelH/pytorch-multi-class-focal-loss
https://github.com/AlexeyAB/darknet
https://github.com/AndresPMD/Clip_CMR
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/AtlasCoCo/Darknet
https://github.com/Burf/tfdetection
https://github.com/CahideSara/deneme
https://github.com/Chang-Chia-Chi/TrackNet-Badminton-Tracking-tensorflow2
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.