songhwanjun / ActiveBiasLinks
☆20Updated 6 years ago
Alternatives and similar repositories for ActiveBias
Users that are interested in ActiveBias are comparing it to the libraries listed below
Sorting:
- [TPAMI2022 & NeurIPS2020] Official implementation of Self-Adaptive Training☆129Updated 3 years ago
- Generalizing to unseen domains via distribution matching☆72Updated 5 years ago
- [NeurIPS 2017] [ICML 2019] Code for complementary-label learning☆49Updated last month
- Domain Generalization via Model-Agnostic Learning of Semantic Features☆148Updated 2 years ago
- ICML'19 How does Disagreement Help Generalization against Label Corruption?☆89Updated 6 years ago
- Reimplementation of "Realistic Evaluation of Deep Semi-Supervised Learning Algorithms"☆80Updated 5 years ago
- Description Code for the paper "Robust Inference via Generative Classifiers for Handling Noisy Labels".☆32Updated 6 years ago
- PyTorch implementation of the paper "Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels" in NIPS 2018☆130Updated 5 years ago
- Sinkhorn Label Allocation is a label assignment method for semi-supervised self-training algorithms. The SLA algorithm is described in fu…☆53Updated 4 years ago
- ICML 2019: Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels☆91Updated 4 years ago
- Code for "Out-of-Distribution Detection Using an Ensemble of Self Supervised Leave-out Classifiers"☆27Updated 3 years ago
- Code for the paper "Distribution Aligning Refinery of Pseudo-label for Imbalanced Semi-supervised Learning" (NeurIPS 20)☆73Updated 3 years ago
- Official PyTorch implementation of the Fishr regularization for out-of-distribution generalization☆88Updated 3 years ago
- ICLR 2021, "Learning with feature-dependent label noise: a progressive approach"☆43Updated 2 years ago
- ☆46Updated 4 years ago
- ICML'19: How does Disagreement Help Generalization against Label Corruption?☆22Updated 6 years ago
- Feature-Critic Networks for Heterogeneous Domain Generalisation☆53Updated 6 years ago
- ☆37Updated 4 years ago
- Toolkit for building machine learning models that generalize to unseen domains and are robust to privacy and other attacks.☆175Updated 2 years ago
- A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks☆231Updated 6 years ago
- On the Importance of Gradients for Detecting Distributional Shifts in the Wild☆56Updated 3 years ago
- Code for our ECCV paper -- "Learning to Balance Specificity and Invariance for In and Out of Domain Generalization"☆56Updated 5 years ago
- AAAI 2021: Beyond Class-Conditional Assumption: A Primary Attempt to Combat Instance-Dependent Label Noise☆35Updated 4 years ago
- Code released for ICML 2019 paper "Bridging Theory and Algorithm for Domain Adaptation".☆138Updated 6 years ago
- Code for the paper: On Symmetric Losses for Learning from Corrupted Labels☆19Updated 6 years ago
- ☆130Updated 2 years ago
- Example implementation for the paper: (ICLR Oral) Learning Robust Representations by Projecting Superficial Statistics Out☆27Updated 4 years ago
- [AAAI 2021] Curriculum Labeling: Revisiting Pseudo-Labeling for Semi-Supervised Learning☆139Updated 4 years ago
- Training Confidence-Calibrated Classifier for Detecting Out-of-Distribution Samples / ICLR 2018☆182Updated 5 years ago
- Virtual Adversarial Training (VAT) for semi-supervised MNIST written in PyTorch: https://arxiv.org/abs/1704.03976☆25Updated 6 years ago