Alternatives and similar repositories for glc:

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

• udibr / noisy_labels
  TRAINING DEEP NEURAL-NETWORKS USING A NOISE ADAPTATION LAYER
  ☆118Updated 7 years ago
• giorgiop / loss-correction
  Robust loss functions for deep neural networks (CVPR 2017)
  ☆91Updated 4 years ago
• ricvolpi / generalize-unseen-domains
  Code for the paper "Generalizing to Unseen Domains via Adversarial Data Augmentation", NeurIPS 2018
  ☆121Updated 4 years ago
• thuml / Transferable-Adversarial-Training
  Code release for Transferable Adversarial Training: A General Approach to Adapting Deep Classifiers (ICML2019)
  ☆80Updated 5 years ago
• 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
• 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
  ☆178Updated 4 years ago
• Newbeeer / L_DMI
  Code for NeurIPS 2019 Paper, "L_DMI: An Information-theoretic Noise-robust Loss Function"
  ☆118Updated last year
• LiJunnan1992 / MLNT
  Meta-Learning based Noise-Tolerant Training
  ☆123Updated 4 years ago
• ShiyuLiang / odin-pytorch
  Principled Detection of Out-of-Distribution Examples in Neural Networks
  ☆201Updated 7 years ago
• chenpf1025 / noisy_label_understanding_utilizing
  ICML 2019: Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels
  ☆90Updated 4 years ago
• GuokaiLiu / Noisy-Labels-Problem-Collection
  This is a collection of Papers and Codes for Noisy Labels Problem.
  ☆61Updated 6 years ago
• ashafaei / OD-test
  OD-test: A Less Biased Evaluation of Out-of-Distribution (Outlier) Detectors (PyTorch)
  ☆62Updated last year
• xingjunm / dimensionality-driven-learning
  Code for paper "Dimensionality-Driven Learning with Noisy Labels" - ICML 2018
  ☆59Updated 7 months ago
• 1Konny / WAE-pytorch
  ☆68Updated 5 years ago
• yogeshbalaji / Meta-Learning-Domain-Generalization
  ☆29Updated 6 years ago
• j-min / Dropouts
  PyTorch Implementations of Dropout Variants
  ☆87Updated 7 years ago
• salesforce / corr_based_prediction
  This repo provides code used in the paper "Predicting with High Correlation Features" (https://arxiv.org/abs/1910.00164):
  ☆54Updated 5 years ago
• 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
• kylehkhsu / cactus-protonets
  Code for Unsupervised Learning via Meta-Learning.
  ☆65Updated 4 years ago
• reiinakano / invariant-risk-minimization
  Implementation of Invariant Risk Minimization https://arxiv.org/abs/1907.02893
  ☆85Updated 4 years ago
• naoto0804 / pytorch-VAT
  Unofficial pytorch implementation of a paper, Distributional Smoothing with Virtual Adversarial Training [Miyato+, ICLR2016].
  ☆26Updated 6 years ago
• P2333 / Max-Mahalanobis-Training
  Max Mahalanobis Training (ICML 2018 + ICLR 2020)
  ☆90Updated 4 years ago
• xingruiyu / coteaching_plus
  ICML'19 How does Disagreement Help Generalization against Label Corruption?
  ☆83Updated 5 years ago
• kylehkhsu / cactus-maml
  Code for Unsupervised Learning via Meta-Learning.
  ☆119Updated 5 years ago
• shaohua0116 / MMAML-Classification
  An official PyTorch implementation of “Multimodal Model-Agnostic Meta-Learning via Task-Aware Modulation” (NeurIPS 2019) by Risto Vuorio*…
  ☆139Updated 5 years ago
• SITE5039 / AdaMixUp
  ☆14Updated 5 years ago
• 11-626 / Deep-INFOMAX
  This is a pytorch implementation of Deep-INFOMAX.
  ☆35Updated 5 years ago
• pokaxpoka / RoGNoisyLabel
  Description Code for the paper "Robust Inference via Generative Classifiers for Handling Noisy Labels".
  ☆32Updated 5 years ago