Alternatives and similar repositories for ddgsd-pytorch

Users that are interested in ddgsd-pytorch are comparing it to the libraries listed below

• Kthyeon / FINE_official
  NeurIPS 2021, "Fine Samples for Learning with Noisy Labels"
  ☆39Updated 3 years ago
• alinlab / cs-kd
  Regularizing Class-wise Predictions via Self-knowledge Distillation (CVPR 2020)
  ☆108Updated 5 years ago
• jiequancui / ResLT
  ResLT: Residual Learning for Long-tailed Recognition (TPAMI 2022)
  ☆61Updated last year
• lgcnsai / PS-KD-Pytorch
  Official PyTorch implementation of PS-KD
  ☆89Updated 3 years ago
• BIT-DA / MetaSAug
  [CVPR 2021] MetaSAug: Meta Semantic Augmentation for Long-Tailed Visual Recognition
  ☆61Updated 2 years ago
• naver-ai / cmo
  ☆47Updated 2 years ago
• hyperconnect / LADE
  This repository contains code for the paper "Disentangling Label Distribution for Long-tailed Visual Recognition", published at CVPR' 202…
  ☆102Updated 2 years ago
• afm-shahab-uddin / SaliencyMix
  SaliencyMix: A Saliency Guided Data Augmentation Strategy for Better Regularization
  ☆49Updated 2 years ago
• dvlab-research / MiSLAS
  Improving Calibration for Long-Tailed Recognition (CVPR2021)
  ☆148Updated 3 years ago
• ShikunLi / Sel-CL
  CVPR 2022: Selective-Supervised Contrastive Learning with Noisy Labels
  ☆93Updated 3 years ago
• EricZsy / BalancedKnowledgeDistillation
  Code for "Balanced Knowledge Distillation for Long-tailed Learning"
  ☆27Updated last year
• xiangly55 / LFME
  Code for "Learning From Multiple Experts: Self-paced Knowledge Distillation for Long-tailed Classification", ECCV 2020 Spotlight
  ☆38Updated 4 years ago
• nukezil / IOMatch
  [ICCV 2023 Oral] IOMatch: Simplifying Open-Set Semi-Supervised Learning with Joint Inliers and Outliers Utilization
  ☆51Updated last year
• sundw2014 / DCM
  Knowledge Transfer via Dense Cross-layer Mutual-distillation (ECCV'2020)
  ☆29Updated 4 years ago
• deeplearning-wisc / react
  Code for NeurIPS 2021 paper "ReAct: Out-of-distribution Detection With Rectified Activations"
  ☆53Updated 3 years ago
• VITA-Group / MAK
  [NeurIPS 2021] “Improving Contrastive Learning on Imbalanced Data via Open-World Sampling”, Ziyu Jiang, Tianlong Chen, Ting Chen, Zhangya…
  ☆28Updated 3 years ago
• dnap512 / SelfReg
  PyTorch implementation of Self-supervised Contrastive Regularization for DG (SelfReg) [ICCV2021]
  ☆77Updated 3 years ago
• AlanChou / Super-Loss
  PyTorch implementation of the paper "SuperLoss: A Generic Loss for Robust Curriculum Learning" in NIPS 2020.
  ☆29Updated 4 years ago
• JoyHuYY1412 / DDE_CIL
  ☆61Updated 3 years ago
• Lee-Gihun / MixCo-Mixup-Contrast
  (NeurIPS 2020 Workshop on SSL) Official Implementation of "MixCo: Mix-up Contrastive Learning for Visual Representation"
  ☆58Updated 2 years ago
• kakaoenterprise / Learning-Debiased-Disentangled
  Official Pytorch implementation of "Learning Debiased Representation via Disentangled Feature Augmentation (Neurips 2021, Oral)"
  ☆102Updated 2 years ago
• jingyang2017 / SRD_ossl
  ☆27Updated 3 years ago
• huangjk97 / T2T
  Code for the paper: "Trash to Treasure: Harvesting OOD Data with Cross-Modal Matching for Open-Set Semi-Supervised Learning"
  ☆25Updated 3 years ago
• deeplearning-wisc / large_scale_ood
  MOS: Towards Scaling Out-of-distribution Detection for Large Semantic Space
  ☆93Updated 4 years ago
• hyseob / SagNet
  Reducing Domain Gap by Reducing Style Bias (SagNets)
  ☆103Updated 4 years ago
• muse1998 / Source-Free-Domain-Generalization
  An open-world scenario domain generalization code base
  ☆26Updated 2 years ago
• hikvision-research / DomainGeneralization
  An official implementation of ECCV 2022 paper "Attention Diversification for Domain Generalization".
  ☆43Updated 2 years ago
• GenDisc / IVR
  Normalization Matters in Weakly Supervised Object Localization (ICCV 2021)
  ☆11Updated 3 years ago
• abdullahjamal / Longtail_DA
  [CVPR 2020] Rethinking Class-Balanced Methods for Long-Tailed Visual Recognition from a Domain Adaptation Perspective
  ☆24Updated 5 years ago
• AnTuo1998 / AE-KD
  ☆27Updated 4 years ago