Alternatives and similar repositories for noisy_labels

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

• mmazeika / glc
  Gold Loss Correction
  ☆87Updated 6 years ago
• 9310gaurav / virtual-adversarial-training
  Pytorch implementation of Virtual Adversarial Training
  ☆134Updated 6 years ago
• s-laine / tempens
  Temporal ensembling for semi-supervised learning
  ☆159Updated 8 years ago
• RuiShu / dirt-t
  A DIRT-T Approach to Unsupervised Domain Adaptation (ICLR 2018)
  ☆176Updated 7 years ago
• danieltan07 / learning-to-reweight-examples
  PyTorch Implementation of the paper Learning to Reweight Examples for Robust Deep Learning
  ☆354Updated 6 years ago
• giorgiop / loss-correction
  Robust loss functions for deep neural networks (CVPR 2017)
  ☆91Updated 5 years ago
• benathi / fastswa-semi-sup
  Improving Consistency-Based Semi-Supervised Learning with Weight Averaging
  ☆186Updated 6 years ago
• thuml / MTlearn
  Code release for "Learning Multiple Tasks with Multilinear Relationship Networks" (NIPS 2017)
  ☆71Updated 7 years ago
• Newbeeer / L_DMI
  Code for NeurIPS 2019 Paper, "L_DMI: An Information-theoretic Noise-robust Loss Function"
  ☆119Updated 2 years ago
• tensorfreitas / Temporal-Ensembling-for-Semi-Supervised-Learning
  Implementation of Temporal Ensembling for Semi-Supervised Learning by Laine et al. with tensorflow eager execution
  ☆55Updated 5 years ago
• lyakaap / VAT-pytorch
  Virtual Adversarial Training (VAT) implementation for PyTorch
  ☆295Updated 6 years ago
• GuokaiLiu / Noisy-Labels-Problem-Collection
  This is a collection of Papers and Codes for Noisy Labels Problem.
  ☆63Updated 7 years ago
• chenpf1025 / noisy_label_understanding_utilizing
  ICML 2019: Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels
  ☆91Updated 4 years ago
• uber-research / learning-to-reweight-examples
  Code for paper "Learning to Reweight Examples for Robust Deep Learning"
  ☆269Updated 6 years ago
• ricvolpi / generalize-unseen-domains
  Code for the paper "Generalizing to Unseen Domains via Adversarial Data Augmentation", NeurIPS 2018
  ☆121Updated 5 years ago
• alinlab / Confident_classifier
  Training Confidence-Calibrated Classifier for Detecting Out-of-Distribution Samples / ICLR 2018
  ☆182Updated 5 years ago
• bhanML / coteaching_plus
  ICML'19: How does Disagreement Help Generalization against Label Corruption?
  ☆21Updated 6 years ago
• ShiyuLiang / odin-pytorch
  Principled Detection of Out-of-Distribution Examples in Neural Networks
  ☆202Updated 8 years ago
• takerum / vat_tf
  Virtual adversarial training with Tensorflow
  ☆253Updated 7 years ago
• LiJunnan1992 / MLNT
  Meta-Learning based Noise-Tolerant Training
  ☆126Updated 5 years ago
• xingjunm / dimensionality-driven-learning
  Code for paper "Dimensionality-Driven Learning with Noisy Labels" - ICML 2018
  ☆58Updated last year
• vtddggg / ATDA
  Unofficial Implement of Asymmetric Tri-training for Unsupervised Domain Adaptation
  ☆41Updated 8 years ago
• GRAAL-Research / domain_adversarial_neural_network
  Domain Adaptation Representation Learning Algorithm (as published in JMLR 2016)
  ☆142Updated 7 years ago
• bhanML / Masking
  NeurIPS'18: Masking: A New Perspective of Noisy Supervision
  ☆54Updated 6 years ago
• alinlab / L2T-ww
  Learning What and Where to Transfer (ICML 2019)
  ☆249Updated 4 years ago
• google / mentornet
  Code for MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks
  ☆325Updated 2 years ago
• uoguelph-mlrg / confidence_estimation
  Learning Confidence for Out-of-Distribution Detection in Neural Networks
  ☆275Updated 7 years ago
• ildoonet / unsupervised-data-augmentation
  Unofficial PyTorch Implementation of Unsupervised Data Augmentation.
  ☆148Updated 4 years ago
• lizhitwo / LearningWithoutForgetting
  Repository for the Learning without Forgetting paper, ECCV 2016
  ☆85Updated 5 years ago
• 11-626 / Deep-INFOMAX
  This is a pytorch implementation of Deep-INFOMAX.
  ☆35Updated 5 years ago