Alternatives and similar repositories for cores

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

• UCSC-REAL / cores
  Learning with Instance-Dependent Label Noise: A Sample Sieve Approach (ICLR2021)
  ☆37Updated 4 years ago
• xingruiyu / coteaching_plus
  ICML'19 How does Disagreement Help Generalization against Label Corruption?
  ☆84Updated 5 years ago
• garyzhao / ME-ADA
  "Maximum-Entropy Adversarial Data Augmentation for Improved Generalization and Robustness" (NeurIPS 2020).
  ☆50Updated 4 years ago
• microsoft / MLC
  Meta Label Correction for Noisy Label Learning
  ☆85Updated 2 years ago
• youjiangxu / FaMUS
  ☆28Updated 3 years ago
• salesforce / MoPro
  MoPro: Webly Supervised Learning
  ☆87Updated last week
• stanford-futuredata / sinkhorn-label-allocation
  Sinkhorn Label Allocation is a label assignment method for semi-supervised self-training algorithms. The SLA algorithm is described in fu…
  ☆53Updated 3 years ago
• xiaoboxia / CDR
  ICLR‘2021: Robust Early-learning: Hindering the Memorization of Noisy Labels
  ☆76Updated 3 years ago
• VITA-Group / SDCLR
  [ICML 2021] “ Self-Damaging Contrastive Learning”, Ziyu Jiang, Tianlong Chen, Bobak Mortazavi, Zhangyang Wang
  ☆63Updated 3 years ago
• tmllab / 2021_NeurIPS_PES
  ☆29Updated 2 years ago
• snu-mllab / Co-Mixup
  Official PyTorch implementation of "Co-Mixup: Saliency Guided Joint Mixup with Supermodular Diversity" (ICLR'21 Oral)
  ☆103Updated 3 years ago
• YU1ut / JointOptimization
  Code for 'Joint Optimization Framework for Learning with Noisy Labels'
  ☆38Updated 6 years ago
• shengliu66 / SOP
  Official Implementation of Robust Training under Label Noise by Over-parameterization
  ☆64Updated 2 years ago
• ContrastToDivide / C2D
  PyTorch implementation of "Contrast to Divide: self-supervised pre-training for learning with noisy labels"
  ☆71Updated 4 years ago
• VITA-Group / Adversarial-Contrastive-Learning
  [NeurIPS 2020] “ Robust Pre-Training by Adversarial Contrastive Learning”, Ziyu Jiang, Tianlong Chen, Ting Chen, Zhangyang Wang
  ☆114Updated 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.
  ☆14Updated 4 years ago
• DiegoOrtego / LabelNoiseMOIT
  Official implementation for: "Multi-Objective Interpolation Training for Robustness to Label Noise"
  ☆39Updated 3 years ago
• abhinavagarwalla / swav-cifar10
  SwAV for CIFAR-10, adapted from https://github.com/facebookresearch/swav
  ☆28Updated 3 years ago
• takashiishida / comp
  [NeurIPS 2017] [ICML 2019] Code for complementary-label learning
  ☆48Updated last year
• tmllab / 2021_ICCV_Me-Momentum
  ☆14Updated 2 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
• ShikunLi / Sel-CL
  CVPR 2022: Selective-Supervised Contrastive Learning with Noisy Labels
  ☆92Updated 3 years ago
• dvlab-research / MiSLAS
  Improving Calibration for Long-Tailed Recognition (CVPR2021)
  ☆148Updated 3 years ago
• gorkemalgan / MSLG_noisy_label
  Official code for the paper "Meta Soft Label Generation for Noisy Labels" accepted at ICPR 2020.
  ☆19Updated 4 years ago
• prithv1 / DMG
  Code for our ECCV paper -- "Learning to Balance Specificity and Invariance for In and Out of Domain Generalization"
  ☆56Updated 4 years ago
• deeplearning-wisc / multi-label-ood
  ☆44Updated 3 years ago
• abdullahjamal / Longtail_DA
  [CVPR 2020] Rethinking Class-Balanced Methods for Long-Tailed Visual Recognition from a Domain Adaptation Perspective
  ☆24Updated 4 years ago
• gohsyi / PeerLoss
  Learning with Noisy Labels by adopting a peer prediction loss function.
  ☆35Updated 5 years ago
• pxiangwu / PLC
  ICLR 2021, "Learning with feature-dependent label noise: a progressive approach"
  ☆43Updated 2 years ago
• dvirsamuel / DRO-LT
  Code for our paper: Samuel and Chechik, "Distributional Robustness Loss for Long-tail Learning"
  ☆32Updated 3 years ago