Alternatives and similar repositories for dimensionality-driven-learning

Users that are interested in dimensionality-driven-learning are comparing it to the libraries listed below

• mmazeika / glc
  Gold Loss Correction
  ☆88Updated 7 years ago
• Newbeeer / L_DMI
  Code for NeurIPS 2019 Paper, "L_DMI: An Information-theoretic Noise-robust Loss Function"
  ☆119Updated 2 years ago
• hendrycks / pre-training
  Pre-Training Buys Better Robustness and Uncertainty Estimates (ICML 2019)
  ☆100Updated 3 years ago
• ricvolpi / generalize-unseen-domains
  Code for the paper "Generalizing to Unseen Domains via Adversarial Data Augmentation", NeurIPS 2018
  ☆121Updated 5 years ago
• giorgiop / loss-correction
  Robust loss functions for deep neural networks (CVPR 2017)
  ☆92Updated 5 years ago
• P2333 / Max-Mahalanobis-Training
  Max Mahalanobis Training (ICML 2018 + ICLR 2020)
  ☆90Updated 5 years ago
• udibr / noisy_labels
  TRAINING DEEP NEURAL-NETWORKS USING A NOISE ADAPTATION LAYER
  ☆119Updated 8 years ago
• uber-research / learning-to-reweight-examples
  Code for paper "Learning to Reweight Examples for Robust Deep Learning"
  ☆269Updated 6 years ago
• xuanqing94 / BayesianDefense
  Adv-BNN: Improved Adversarial Defense through Robust Bayesian Neural Network
  ☆61Updated 6 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 8 months ago
• btjhjeon / IMM_tensorflow
  Overcoming Catastrophic Forgetting by Incremental Moment Matching (IMM)
  ☆35Updated 8 years ago
• thuml / Transferable-Adversarial-Training
  Code release for Transferable Adversarial Training: A General Approach to Adapting Deep Classifiers (ICML2019)
  ☆81Updated 6 years ago
• 9310gaurav / virtual-adversarial-training
  Pytorch implementation of Virtual Adversarial Training
  ☆134Updated 6 years ago
• rugrag / learn-unbiased
  Code for the paper Learning Unbiased Representations via Mutual Information Backpropagation
  ☆21Updated 5 years ago
• chenpf1025 / noisy_label_understanding_utilizing
  ICML 2019: Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels
  ☆91Updated 5 years ago
• mtoneva / example_forgetting
  ☆178Updated last year
• zhuchen03 / VIBNet
  Compressing Neural Networks using the Variational Information Bottleneck
  ☆66Updated 3 years ago
• capybaralet / REx_code_release
  ☆63Updated 5 years ago
• danieltan07 / learning-to-reweight-examples
  PyTorch Implementation of the paper Learning to Reweight Examples for Robust Deep Learning
  ☆354Updated 6 years ago
• liulu112601 / Gated-Propagation-Net
  NeurIPS 2019 : Learning to Propagate for Graph Meta-Learning
  ☆36Updated 6 years ago
• alinlab / Confident_classifier
  Training Confidence-Calibrated Classifier for Detecting Out-of-Distribution Samples / ICLR 2018
  ☆182Updated 5 years ago
• kylehkhsu / cactus-maml
  Code for Unsupervised Learning via Meta-Learning.
  ☆123Updated 6 years ago
• thuml / MTlearn
  Code release for "Learning Multiple Tasks with Multilinear Relationship Networks" (NIPS 2017)
  ☆71Updated 8 years ago
• P2333 / Adaptive-Diversity-Promoting
  Adversarial Defense for Ensemble Models (ICML 2019)
  ☆61Updated 5 years ago
• P2333 / Mixup-Inference
  Further improve robustness of mixup-trained models in inference (ICLR 2020)
  ☆60Updated 5 years ago
• xiahaifeng1995 / adversarial-robustness
  ☆26Updated 6 years ago
• bhanML / coteaching_plus
  ICML'19: How does Disagreement Help Generalization against Label Corruption?
  ☆22Updated 6 years ago
• LiJunnan1992 / MLNT
  Meta-Learning based Noise-Tolerant Training
  ☆123Updated 5 years ago
• foolyc / Meta-SGD
  Meta-SGD experiment on Omniglot classification compared with MAML
  ☆82Updated 8 years ago
• GuokaiLiu / Noisy-Labels-Problem-Collection
  This is a collection of Papers and Codes for Noisy Labels Problem.
  ☆63Updated 7 years ago