danieltan07 / learning-to-reweight-examples

Related projects ⓘ

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

• uber-research / learning-to-reweight-examples
  Code for paper "Learning to Reweight Examples for Robust Deep Learning"
  ☆269Updated 5 years ago
• lyakaap / VAT-pytorch
  Virtual Adversarial Training (VAT) implementation for PyTorch
  ☆297Updated 5 years ago
• xjtushujun / meta-weight-net
  NeurIPS'19: Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting (Pytorch implementation for noisy labels).
  ☆281Updated 2 years ago
• PaulAlbert31 / LabelNoiseCorrection
  Official Implementation of ICML 2019 Unsupervised label noise modeling and loss correction
  ☆220Updated 4 years ago
• lorenmt / maxl
  The implementation of "Self-Supervised Generalisation with Meta Auxiliary Learning" [NeurIPS 2019].
  ☆170Updated 2 years ago
• LiJunnan1992 / MLNT
  Meta-Learning based Noise-Tolerant Training
  ☆123Updated 4 years ago
• alinlab / L2T-ww
  Learning What and Where to Transfer (ICML 2019)
  ☆250Updated 4 years ago
• bhanML / Co-teaching
  NeurIPS'18: Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels
  ☆492Updated 3 years ago
• benathi / fastswa-semi-sup
  Improving Consistency-Based Semi-Supervised Learning with Weight Averaging
  ☆185Updated 5 years ago
• 9310gaurav / virtual-adversarial-training
  Pytorch implementation of Virtual Adversarial Training
  ☆133Updated 5 years ago
• mtoneva / example_forgetting
  ☆174Updated 3 months ago
• ildoonet / unsupervised-data-augmentation
  Unofficial PyTorch Implementation of Unsupervised Data Augmentation.
  ☆147Updated 4 years ago
• peisuke / MomentumContrast.pytorch
  Reproduction of Momentum Contrast for Unsupervised Visual Representation Learning
  ☆120Updated 3 months ago
• Newbeeer / L_DMI
  Code for NeurIPS 2019 Paper, "L_DMI: An Information-theoretic Noise-robust Loss Function"
  ☆117Updated last year
• udibr / noisy_labels
  TRAINING DEEP NEURAL-NETWORKS USING A NOISE ADAPTATION LAYER
  ☆118Updated 7 years ago
• alinlab / Confident_classifier
  Training Confidence-Calibrated Classifier for Detecting Out-of-Distribution Samples / ICLR 2018
  ☆178Updated 4 years ago
• DuaneNielsen / DeepInfomaxPytorch
  Learning deep representations by mutual information estimation and maximization
  ☆322Updated 5 years ago
• vikasverma1077 / manifold_mixup
  Code for reproducing Manifold Mixup results (ICML 2019)
  ☆482Updated 7 months ago
• gitabcworld / FewShotLearning
  Pytorch implementation of the paper "Optimization as a Model for Few-Shot Learning"
  ☆256Updated 7 years ago
• csyanbin / TPN
  Code for ICLR19 paper: Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learning.
  ☆243Updated 5 years ago
• renmengye / few-shot-ssl-public
  Meta Learning for Semi-Supervised Few-Shot Classification
  ☆553Updated 5 years ago
• dragen1860 / Reptile-Pytorch
  ☆150Updated 4 years ago
• gorkemalgan / deep_learning_with_noisy_labels_literature
  This repo consists of collection of papers and repos on the topic of deep learning by noisy labels / label noise.
  ☆235Updated 3 years ago
• uoguelph-mlrg / confidence_estimation
  Learning Confidence for Out-of-Distribution Detection in Neural Networks
  ☆267Updated 6 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
  ☆125Updated 5 years ago
• google-research / remixmatch
  ☆130Updated 2 years ago
• google / mentornet
  Code for MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks
  ☆321Updated last year
• chenpf1025 / noisy_label_understanding_utilizing
  ICML 2019: Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels
  ☆90Updated 3 years ago
• mmazeika / glc
  Gold Loss Correction
  ☆86Updated 5 years ago
• yikun2019 / PENCIL
  PyTorch implementation of Probabilistic End-to-end Noise Correction for Learning with Noisy Labels, CVPR 2019.
  ☆139Updated 5 years ago