Alternatives and similar repositories for mentornet

Users that are interested in mentornet 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"
  ☆270Updated 6 years ago
• danieltan07 / learning-to-reweight-examples
  PyTorch Implementation of the paper Learning to Reweight Examples for Robust Deep Learning
  ☆354Updated 6 years ago
• ildoonet / unsupervised-data-augmentation
  Unofficial PyTorch Implementation of Unsupervised Data Augmentation.
  ☆148Updated 5 years ago
• lyakaap / VAT-pytorch
  Virtual Adversarial Training (VAT) implementation for PyTorch
  ☆296Updated 7 years ago
• PaulAlbert31 / LabelNoiseCorrection
  Official Implementation of ICML 2019 Unsupervised label noise modeling and loss correction
  ☆225Updated 5 years ago
• alinlab / Confident_classifier
  Training Confidence-Calibrated Classifier for Detecting Out-of-Distribution Samples / ICLR 2018
  ☆182Updated 5 years ago
• vikasverma1077 / manifold_mixup
  Code for reproducing Manifold Mixup results (ICML 2019)
  ☆495Updated last year
• lorenmt / maxl
  The implementation of "Self-Supervised Generalisation with Meta Auxiliary Learning" [NeurIPS 2019].
  ☆175Updated 4 years ago
• Newbeeer / L_DMI
  Code for NeurIPS 2019 Paper, "L_DMI: An Information-theoretic Noise-robust Loss Function"
  ☆119Updated 2 years ago
• peisuke / MomentumContrast.pytorch
  Reproduction of Momentum Contrast for Unsupervised Visual Representation Learning
  ☆121Updated last year
• Philip-Bachman / amdim-public
  Public repo for Augmented Multiscale Deep InfoMax representation learning
  ☆401Updated 5 years ago
• mtoneva / example_forgetting
  ☆178Updated last year
• LiJunnan1992 / MLNT
  Meta-Learning based Noise-Tolerant Training
  ☆123Updated 5 years ago
• GT-RIPL / L2C
  Learning to Cluster. A deep clustering strategy.
  ☆316Updated 6 years ago
• 9310gaurav / virtual-adversarial-training
  Pytorch implementation of Virtual Adversarial Training
  ☆134Updated 6 years ago
• benathi / fastswa-semi-sup
  Improving Consistency-Based Semi-Supervised Learning with Weight Averaging
  ☆189Updated 6 years ago
• gitabcworld / FewShotLearning
  Pytorch implementation of the paper "Optimization as a Model for Few-Shot Learning"
  ☆263Updated 8 years ago
• csyanbin / TPN
  Code for ICLR19 paper: Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learning.
  ☆244Updated 6 years ago
• google-research / remixmatch
  ☆131Updated 3 years ago
• alinlab / L2T-ww
  Learning What and Where to Transfer (ICML 2019)
  ☆250Updated 5 years ago
• bhanML / Co-teaching
  NeurIPS'18: Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels
  ☆519Updated 4 years ago
• xjtushujun / meta-weight-net
  NeurIPS'19: Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting (Pytorch implementation for noisy labels).
  ☆291Updated 4 years ago
• kuanghuei / clean-net
  Tensorflow source code for "CleanNet: Transfer Learning for Scalable Image Classifier Training with Label Noise" (CVPR 2018)
  ☆88Updated 7 years ago
• uoguelph-mlrg / confidence_estimation
  Learning Confidence for Out-of-Distribution Detection in Neural Networks
  ☆275Updated 7 years ago
• udibr / noisy_labels
  TRAINING DEEP NEURAL-NETWORKS USING A NOISE ADAPTATION LAYER
  ☆119Updated 8 years ago
• chenpf1025 / noisy_label_understanding_utilizing
  ICML 2019: Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels
  ☆91Updated 5 years ago
• sinhasam / vaal
  Variational Adversarial Active Learning (ICCV 2019)
  ☆231Updated 2 years ago
• mangye16 / Unsupervised_Embedding_Learning
  Code for Unsupervised Embedding Learning via Invariant and Spreading Instance Feature
  ☆210Updated 6 years ago
• Clarifai / few-shot-ctm
  Few shot learning
  ☆157Updated 4 years ago
• facebookresearch / low-shot-shrink-hallucinate
  Presenting Low-shot Visual Recognition by Shrinking and Hallucinating Features
  ☆310Updated 4 years ago