Alternatives and similar repositories for symloss-ber-auc:

Users that are interested in symloss-ber-auc are comparing it to the libraries listed below

• HaohanWang / HEX
  Example implementation for the paper: (ICLR Oral) Learning Robust Representations by Projecting Superficial Statistics Out
  ☆27Updated 4 years ago
• haebeom-lee / metadrop
  Tensorflow implementation of "Meta Dropout: Learning to Perturb Latent Features for Generalization" (ICLR 2020)
  ☆27Updated 5 years ago
• naoto0804 / pytorch-VAT
  Unofficial pytorch implementation of a paper, Distributional Smoothing with Virtual Adversarial Training [Miyato+, ICLR2016].
  ☆26Updated 7 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
• christopher-beckham / amr
  Official adversarial mixup resynthesis repository
  ☆35Updated 5 years ago
• hrayrhar / limit-label-memorization
  Improving generalization by controlling label-noise information in neural network weights.
  ☆40Updated 4 years ago
• bhanML / coteaching_plus
  ICML'19: How does Disagreement Help Generalization against Label Corruption?
  ☆21Updated 5 years ago
• cyber-meow / PUbiasedN
  PyTorch implementation for the paper Classification from Positive, Unlabeled and Biased Negative Data.
  ☆19Updated last year
• fmu2 / gradfeat20
  Gradients as Features for Deep Representation Learning
  ☆43Updated 5 years ago
• crashmoon / MAML-Pytorch-Multi-GPUs
  pytorch maml with Multi-GPUs, fast and simplest implementation
  ☆13Updated 4 years ago
• hendrycks / pre-training
  Pre-Training Buys Better Robustness and Uncertainty Estimates (ICML 2019)
  ☆100Updated 3 years ago
• khetan2 / MBEM
  Learning From Noisy Singly-labeled Data
  ☆18Updated 7 years ago
• chenpf1025 / noisy_label_understanding_utilizing
  ICML 2019: Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels
  ☆91Updated 4 years ago
• Newbeeer / Max-MIG
  Code for ICLR 2019 Paper, "MAX-MIG: AN INFORMATION THEORETIC APPROACH FOR JOINT LEARNING FROM CROWDS"
  ☆25Updated last year
• orlitany / SOSELETO
  pytorch implementation of SOSELETO
  ☆15Updated 5 years ago
• mil-tokyo / adr_da
  ☆34Updated 7 years ago
• nabihach / IDA
  ☆13Updated 5 years ago
• LARS-research / S2E
  Q. Yao, H. Yang, B. Han, G. Niu, J. Kwok. Searching to Exploit Memorization Effect in Learning from Noisy Labels. ICML 2020
  ☆22Updated 4 years ago
• alvinchangw / IGAM_CVPR2020
  Implementation for What it Thinks is Important is Important: Robustness Transfers through Input Gradients (CVPR 2020 Oral)
  ☆16Updated 2 years ago
• SITE5039 / AdaMixUp
  ☆14Updated 6 years ago
• SeongokRyu / mutual_information_and_self-supervised_learning
  Notes and tutorials on "Mutual information and self-supervised learning"
  ☆39Updated 5 years ago
• liulu112601 / Gated-Propagation-Net
  NeurIPS 2019 : Learning to Propagate for Graph Meta-Learning
  ☆36Updated 5 years ago
• eladhoffer / fix_your_classifier
  ☆34Updated 6 years ago
• xingjunm / dimensionality-driven-learning
  Code for paper "Dimensionality-Driven Learning with Noisy Labels" - ICML 2018
  ☆58Updated 10 months ago
• bhanML / SIGUA
  ICML'20: SIGUA: Forgetting May Make Learning with Noisy Labels More Robust
  ☆15Updated 4 years ago
• gohsyi / PeerLoss
  Learning with Noisy Labels by adopting a peer prediction loss function.
  ☆35Updated 5 years ago
• pokaxpoka / RoGNoisyLabel
  Description Code for the paper "Robust Inference via Generative Classifiers for Handling Noisy Labels".
  ☆32Updated 5 years ago
• littleredxh / HardNegative
  ☆51Updated 4 years ago
• Manuscrit / Area-Under-the-Margin-Ranking
  Implementation of the paper Identifying Mislabeled Data using the Area Under the Margin Ranking: https://arxiv.org/pdf/2001.10528v2.pdf
  ☆21Updated 5 years ago
• M-Elfeki / GDPP
  Generator loss to reduce mode-collapse and to improve the generated samples quality.
  ☆34Updated 5 years ago