Alternatives and similar repositories for PRODEN

Users that are interested in PRODEN are comparing it to the libraries listed below

• hongwei-wen / LW-loss-for-partial-label
  ☆24Updated 3 years ago
• palm-ml / valen
  Instance-Dependent Partial Label Learning（NIPS'21）；Variational Label Enhancement for Instance-Dependent Partial Label Learning（TPAMI）
  ☆26Updated 6 months ago
• xiaoboxia / CDR
  ICLR‘2021: Robust Early-learning: Hindering the Memorization of Noisy Labels
  ☆78Updated 4 years ago
• hbzju / PiCO
  PyTorch implementation of PiCO https://arxiv.org/abs/2201.08984
  ☆226Updated last year
• bhanML / label-noise-papers
  An update-to-date list for papers related with label-noise representation learning is here.
  ☆91Updated 4 years ago
• ShiYunyi / Meta-Weight-Net_Code-Optimization
  A new code framework that uses pytorch to implement meta-learning, and takes Meta-Weight-Net as an example.
  ☆62Updated 4 years ago
• guolz-ml / DS3L
  The implementation of the algorithm in the paper "Safe Deep Semi-Supervised Learning for Unseen-Class Unlabeled Data" published in ICML 2…
  ☆50Updated 5 years ago
• UCSC-REAL / cores
  Learning with Instance-Dependent Label Noise: A Sample Sieve Approach (ICLR2021)
  ☆38Updated 4 years ago
• xiemk / awesome-weakly-supervised-multi-label-learning
  ☆62Updated last year
• ShuoYang-1998 / BLTM
  [ICML'2022] Estimating Instance-dependent Bayes-label Transition Matrix using a Deep Neural Network
  ☆21Updated 3 years ago
• ShikunLi / Sel-CL
  CVPR 2022: Selective-Supervised Contrastive Learning with Noisy Labels
  ☆95Updated 3 years ago
• xiemk / SPML-LAC
  ☆16Updated 2 years ago
• chenpf1025 / IDN
  AAAI 2021: Beyond Class-Conditional Assumption: A Primary Attempt to Combat Instance-Dependent Label Noise
  ☆35Updated 4 years ago
• wu-dd / Advances-in-Partial-and-Complementary-Label-Learning
  A curated list of most recent papers & codes in Learning with Partial/Complementary Labels
  ☆70Updated 3 months ago
• SII-Ferenas / CAVL
  Exploiting Class Activation Value for Partial-Label Learning, ICLR 2022 (poster)
  ☆14Updated 3 years ago
• LAMDA-CL / Awesome-open-world-learning
  Awesome-open-world-learning
  ☆26Updated 4 years ago
• wwangwitsel / PARM
  [NeurIPS'20] Semi-Supervised Partial Label Learning via Confidence-Rated Margin Maximization
  ☆21Updated 3 years ago
• hbzju / SoLar
  Source code for NeurIPS 2022 paper SoLar
  ☆30Updated 2 years ago
• Hao-Ning / MEIDTM-Instance-Dependent-Label-Noise-Learning-with-Manifold-Regularized-Transition-Matrix-Estimatio
  pytorch
  ☆10Updated 3 years ago
• guolz-ml / Survey-on-Robust-Semi-Supervised-Learning
  Survey on Robust Weakly Supervised Learning
  ☆13Updated 4 years ago
• weitongseu / PCL
  ☆10Updated 3 years ago
• WuYichen-97 / Learning-to-Purify-Noisy-Labels-via-Meta-Soft-Label-Corrector
  [AAAI 21] Utilizing meta-learning to correct the noisy labels.
  ☆15Updated 4 years ago
• takashiishida / comp
  [NeurIPS 2017] [ICML 2019] Code for complementary-label learning
  ☆50Updated 4 months ago
• LTH14 / targeted-supcon
  A PyTorch implementation of the paper Targeted Supervised Contrastive Learning for Long-tailed Recognition
  ☆104Updated 2 years ago
• microsoft / MLC
  Meta Label Correction for Noisy Label Learning
  ☆87Updated 3 years ago
• Chumsy0725 / logit-adj-pytorch
  PyTorch implementation of the paper: Long-tail Learning via Logit Adjustment
  ☆116Updated 4 years ago
• xiaoboxia / Classification-with-noisy-labels-by-importance-reweighting
  TPAMI: Classification with noisy labels by importance reweighting.
  ☆40Updated 6 years ago
• linyongver / Bayesian-Invariant-Risk-Minmization
  This is the code for the paper Bayesian Invariant Risk Minmization of CVPR 2022.
  ☆47Updated 2 years ago
• salesforce / RRL
  ☆20Updated 7 months ago
• tim-learn / ATDOC
  code released for our CVPR 2021 paper "Domain Adaptation with Auxiliary Target Domain-Oriented Classifier"
  ☆59Updated 2 years ago