xingjunm / dimensionality-driven-learningLinks
Code for paper "Dimensionality-Driven Learning with Noisy Labels" - ICML 2018
☆58Updated last year
Alternatives and similar repositories for dimensionality-driven-learning
Users that are interested in dimensionality-driven-learning are comparing it to the libraries listed below
Sorting:
- Gold Loss Correction☆87Updated 6 years ago
- Code for NeurIPS 2019 Paper, "L_DMI: An Information-theoretic Noise-robust Loss Function"☆119Updated 2 years ago
- Pre-Training Buys Better Robustness and Uncertainty Estimates (ICML 2019)☆100Updated 3 years ago
- Robust loss functions for deep neural networks (CVPR 2017)☆91Updated 5 years ago
- ☆175Updated last year
- Code for the paper "Generalizing to Unseen Domains via Adversarial Data Augmentation", NeurIPS 2018☆121Updated 5 years ago
- Max Mahalanobis Training (ICML 2018 + ICLR 2020)☆90Updated 4 years ago
- Adv-BNN: Improved Adversarial Defense through Robust Bayesian Neural Network☆62Updated 6 years ago
- Code for the paper Learning Unbiased Representations via Mutual Information Backpropagation☆21Updated 5 years ago
- Code release for Transferable Adversarial Training: A General Approach to Adapting Deep Classifiers (ICML2019)☆81Updated 6 years ago
- Code for Unsupervised Learning via Meta-Learning.☆121Updated 6 years ago
- Overcoming Catastrophic Forgetting by Incremental Moment Matching (IMM)☆35Updated 7 years ago
- This repo provides code used in the paper "Predicting with High Correlation Features" (https://arxiv.org/abs/1910.00164):☆54Updated 3 months ago
- Pytorch implementation of Virtual Adversarial Training☆134Updated 6 years ago
- TRAINING DEEP NEURAL-NETWORKS USING A NOISE ADAPTATION LAYER☆118Updated 8 years ago
- NeurIPS'18: Masking: A New Perspective of Noisy Supervision☆54Updated 6 years ago
- CVPR'19 experiments with (on-manifold) adversarial examples.☆45Updated 5 years ago
- Further improve robustness of mixup-trained models in inference (ICLR 2020)☆60Updated 5 years ago
- ☆63Updated 4 years ago
- PyTorch Implementation of the paper Learning to Reweight Examples for Robust Deep Learning☆353Updated 6 years ago
- SGD and Ordered SGD codes for deep learning, SVM, and logistic regression☆35Updated 4 years ago
- ☆25Updated 6 years ago
- A DIRT-T Approach to Unsupervised Domain Adaptation (ICLR 2018)☆175Updated 7 years ago
- Adversarial Defense for Ensemble Models (ICML 2019)☆61Updated 4 years ago
- Code for paper "Learning to Reweight Examples for Robust Deep Learning"☆269Updated 6 years ago
- Gradients as Features for Deep Representation Learning☆43Updated 5 years ago
- ICML'19: How does Disagreement Help Generalization against Label Corruption?☆21Updated 6 years ago
- ICML 2019: Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels☆91Updated 4 years ago
- Project page for our paper: Interpreting Adversarially Trained Convolutional Neural Networks☆66Updated 5 years ago
- Code accompanying the ICML-2018 paper "Gradient-Based Meta-Learning with Learned Layerwise Metric and Subspace"☆38Updated 6 years ago