Alternatives and similar repositories for learning-to-reweight-examples

Users that are interested in learning-to-reweight-examples are comparing it to the libraries listed below

• uber-research / learning-to-reweight-examples
  Code for paper "Learning to Reweight Examples for Robust Deep Learning"
  ☆269Updated 6 years ago
• lyakaap / VAT-pytorch
  Virtual Adversarial Training (VAT) implementation for PyTorch
  ☆296Updated 6 years ago
• xjtushujun / meta-weight-net
  NeurIPS'19: Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting (Pytorch implementation for noisy labels).
  ☆289Updated 3 years ago
• PaulAlbert31 / LabelNoiseCorrection
  Official Implementation of ICML 2019 Unsupervised label noise modeling and loss correction
  ☆223Updated 4 years ago
• LiJunnan1992 / MLNT
  Meta-Learning based Noise-Tolerant Training
  ☆125Updated 4 years ago
• ildoonet / unsupervised-data-augmentation
  Unofficial PyTorch Implementation of Unsupervised Data Augmentation.
  ☆147Updated 4 years ago
• Newbeeer / L_DMI
  Code for NeurIPS 2019 Paper, "L_DMI: An Information-theoretic Noise-robust Loss Function"
  ☆119Updated 2 years ago
• alinlab / L2T-ww
  Learning What and Where to Transfer (ICML 2019)
  ☆248Updated 4 years ago
• google / mentornet
  Code for MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks
  ☆323Updated 2 years ago
• DuaneNielsen / DeepInfomaxPytorch
  Learning deep representations by mutual information estimation and maximization
  ☆324Updated 6 years ago
• uoguelph-mlrg / confidence_estimation
  Learning Confidence for Out-of-Distribution Detection in Neural Networks
  ☆275Updated 7 years ago
• benathi / fastswa-semi-sup
  Improving Consistency-Based Semi-Supervised Learning with Weight Averaging
  ☆186Updated 6 years ago
• csyanbin / TPN
  Code for ICLR19 paper: Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learning.
  ☆242Updated 6 years ago
• 9310gaurav / virtual-adversarial-training
  Pytorch implementation of Virtual Adversarial Training
  ☆134Updated 6 years ago
• bhanML / Co-teaching
  NeurIPS'18: Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels
  ☆506Updated 3 years ago
• mmazeika / glc
  Gold Loss Correction
  ☆87Updated 6 years ago
• peisuke / MomentumContrast.pytorch
  Reproduction of Momentum Contrast for Unsupervised Visual Representation Learning
  ☆120Updated 11 months ago
• vikasverma1077 / manifold_mixup
  Code for reproducing Manifold Mixup results (ICML 2019)
  ☆494Updated last year
• lorenmt / maxl
  The implementation of "Self-Supervised Generalisation with Meta Auxiliary Learning" [NeurIPS 2019].
  ☆175Updated 3 years ago
• AlanChou / Truncated-Loss
  PyTorch implementation of the paper "Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels" in NIPS 2018
  ☆128Updated 5 years ago
• hongyi-zhang / mixup
  Implementation of the mixup training method
  ☆466Updated 7 years ago
• udibr / noisy_labels
  TRAINING DEEP NEURAL-NETWORKS USING A NOISE ADAPTATION LAYER
  ☆118Updated 8 years ago
• yikun2019 / PENCIL
  PyTorch implementation of Probabilistic End-to-end Noise Correction for Learning with Noisy Labels, CVPR 2019.
  ☆139Updated 5 years ago
• ferretj / temporal-ensembling
  PyTorch implementation of Temporal Ensembling for Semi-Supervised Learning
  ☆111Updated 6 years ago
• gitabcworld / FewShotLearning
  Pytorch implementation of the paper "Optimization as a Model for Few-Shot Learning"
  ☆257Updated 7 years ago
• google-research / remixmatch
  ☆129Updated 2 years ago
• dragen1860 / Reptile-Pytorch
  ☆154Updated 5 years ago
• dragen1860 / LearningToCompare-Pytorch
  Pytorch Implementation for CVPR2018 Paper: Learning to Compare: Relation Network for Few-Shot Learning
  ☆250Updated 7 years ago
• RuiShu / dirt-t
  A DIRT-T Approach to Unsupervised Domain Adaptation (ICLR 2018)
  ☆175Updated 7 years ago
• Philip-Bachman / amdim-public
  Public repo for Augmented Multiscale Deep InfoMax representation learning
  ☆400Updated 4 years ago