Alternatives and similar repositories for loss-correction:

Users that are interested in loss-correction are comparing it to the libraries listed below

• mmazeika / glc
  Gold Loss Correction
  ☆86Updated 6 years ago
• udibr / noisy_labels
  TRAINING DEEP NEURAL-NETWORKS USING A NOISE ADAPTATION LAYER
  ☆118Updated 7 years ago
• ShiyuLiang / odin-pytorch
  Principled Detection of Out-of-Distribution Examples in Neural Networks
  ☆201Updated 7 years ago
• LiJunnan1992 / MLNT
  Meta-Learning based Noise-Tolerant Training
  ☆123Updated 4 years ago
• Cysu / noisy_label
  Code for the CVPR15 paper "Learning from Massive Noisy Labeled Data for Image Classification"
  ☆117Updated 6 years ago
• thuml / Transferable-Adversarial-Training
  Code release for Transferable Adversarial Training: A General Approach to Adapting Deep Classifiers (ICML2019)
  ☆81Updated 5 years ago
• ricvolpi / generalize-unseen-domains
  Code for the paper "Generalizing to Unseen Domains via Adversarial Data Augmentation", NeurIPS 2018
  ☆121Updated 4 years ago
• 9310gaurav / virtual-adversarial-training
  Pytorch implementation of Virtual Adversarial Training
  ☆133Updated 5 years ago
• alinlab / Confident_classifier
  Training Confidence-Calibrated Classifier for Detecting Out-of-Distribution Samples / ICLR 2018
  ☆179Updated 4 years ago
• DaikiTanaka-UT / JointOptimization
  Joint Optimization Framework for Learning with Noisy Labels
  ☆45Updated 6 years ago
• yogeshbalaji / Meta-Learning-Domain-Generalization
  ☆29Updated 6 years ago
• chenpf1025 / noisy_label_understanding_utilizing
  ICML 2019: Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels
  ☆90Updated 4 years ago
• hendrycks / pre-training
  Pre-Training Buys Better Robustness and Uncertainty Estimates (ICML 2019)
  ☆100Updated 3 years ago
• Newbeeer / L_DMI
  Code for NeurIPS 2019 Paper, "L_DMI: An Information-theoretic Noise-robust Loss Function"
  ☆118Updated last year
• xingjunm / dimensionality-driven-learning
  Code for paper "Dimensionality-Driven Learning with Noisy Labels" - ICML 2018
  ☆58Updated 9 months ago
• takerum / vat
  Code for reproducing the results on the MNIST dataset in the paper "Distributional Smoothing with Virtual Adversarial Training"
  ☆110Updated 7 years ago
• danieltan07 / learning-to-reweight-examples
  PyTorch Implementation of the paper Learning to Reweight Examples for Robust Deep Learning
  ☆354Updated 5 years ago
• bhanML / Masking
  NeurIPS'18: Masking: A New Perspective of Noisy Supervision
  ☆54Updated 6 years ago
• ashafaei / OD-test
  OD-test: A Less Biased Evaluation of Out-of-Distribution (Outlier) Detectors (PyTorch)
  ☆62Updated last year
• RuiShu / dirt-t
  A DIRT-T Approach to Unsupervised Domain Adaptation (ICLR 2018)
  ☆175Updated 6 years ago
• bhanML / coteaching_plus
  ICML'19: How does Disagreement Help Generalization against Label Corruption?
  ☆21Updated 5 years ago
• xingruiyu / coteaching_plus
  ICML'19 How does Disagreement Help Generalization against Label Corruption?
  ☆84Updated 5 years ago
• xinzheli1217 / learning-to-self-train
  Learning to Self-Train for Semi-Supervised Few-Shot
  ☆93Updated 2 years ago
• Britefury / self-ensemble-visual-domain-adapt
  Code repository for the small image experiments our paper 'Self-ensembling for Domain Adaptation'
  ☆193Updated 5 years ago
• ashafaei / out-of-distribution-detection
  The Ultimate Reference for Out of Distribution Detection with Deep Neural Networks
  ☆118Updated 5 years ago
• xinmei9322 / SNTG
  ☆44Updated 6 years ago
• btjhjeon / IMM_tensorflow
  Overcoming Catastrophic Forgetting by Incremental Moment Matching (IMM)
  ☆34Updated 7 years ago
• takerum / vat_chainer
  Implementation of Virtual adversarial training with chainer
  ☆40Updated 7 years ago
• brjathu / RPSnet
  Official Implementation of "Random Path Selection for Incremental Learning" paper. NeurIPS 2019
  ☆53Updated 2 years ago
• benathi / fastswa-semi-sup
  Improving Consistency-Based Semi-Supervised Learning with Weight Averaging
  ☆185Updated 6 years ago