sinhasam / vaal
Variational Adversarial Active Learning (ICCV 2019)
☆225Updated last year
Related projects ⓘ
Alternatives and complementary repositories for vaal
- Source code for ICLR 2018 Paper: Active Learning for Convolutional Neural Networks: A Core-Set Approach☆260Updated 6 years ago
- Reproducing experimental results of LL4AL [Yoo et al. 2019 CVPR]☆215Updated 4 years ago
- Code and website for DAL (Discriminative Active Learning) - a new active learning algorithm for neural networks in the batch setting. For…☆202Updated 5 years ago
- An implementation of the BADGE batch active learning algorithm.☆197Updated 5 months ago
- Self-Supervised Learning for OOD Detection (NeurIPS 2019)☆266Updated 3 years ago
- PyTorch implementation of the paper "Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels" in NIPS 2018☆125Updated 5 years ago
- Official implementation of "Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning"☆153Updated 4 years ago
- Official Implementation of ICML 2019 Unsupervised label noise modeling and loss correction☆220Updated 4 years ago
- Meta-Learning based Noise-Tolerant Training☆123Updated 4 years ago
- ☆130Updated 2 years ago
- "Automatically Discovering and Learning New Visual Categories with Ranking Statistics" by Kai Han, Sylvestre-Alvise Rebuffi, Sebastien Eh…☆224Updated 4 years ago
- PyTorch implementation of Probabilistic End-to-end Noise Correction for Learning with Noisy Labels, CVPR 2019.☆139Updated 5 years ago
- Learning Confidence for Out-of-Distribution Detection in Neural Networks☆267Updated 6 years ago
- PyTorch Implementation of the paper Learning to Reweight Examples for Robust Deep Learning☆353Updated 5 years ago
- Official Implementation of Early-Learning Regularization Prevents Memorization of Noisy Labels☆288Updated last year
- NeurIPS'19: Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting (Pytorch implementation for noisy labels).☆281Updated 2 years ago
- Reproduce Results for ICCV2019 "Symmetric Cross Entropy for Robust Learning with Noisy Labels" https://arxiv.org/abs/1908.06112☆184Updated 3 years ago
- Code for the paper "A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks".☆342Updated 5 years ago
- Awesome Active Learning Paper List☆139Updated 6 months ago
- Virtual Adversarial Training (VAT) implementation for PyTorch☆297Updated 5 years ago
- Reproduction of Momentum Contrast for Unsupervised Visual Representation Learning☆120Updated 3 months ago
- PyTorch implementation of consistency regularization methods for semi-supervised learning☆77Updated 4 years ago
- A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks☆224Updated 5 years ago
- Addressing Failure Prediction by Learning Model Confidence☆167Updated last year
- Improving Consistency-Based Semi-Supervised Learning with Weight Averaging☆185Updated 5 years ago
- Code for paper: DivideMix: Learning with Noisy Labels as Semi-supervised Learning☆543Updated 4 years ago
- Pytorch implementation of Virtual Adversarial Training☆133Updated 5 years ago
- Training Confidence-Calibrated Classifier for Detecting Out-of-Distribution Samples / ICLR 2018☆178Updated 4 years ago
- PyTorch implementation of Temporal Ensembling for Semi-Supervised Learning☆109Updated 6 years ago
- Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning.☆230Updated 5 months ago