AAAI2018submission / Adversarial_Active_LearningLinks
Adversarial Active Learning for Deep Networks
☆14Updated 6 years ago
Alternatives and similar repositories for Adversarial_Active_Learning
Users that are interested in Adversarial_Active_Learning are comparing it to the libraries listed below
Sorting:
- Source code for ICLR 2018 Paper: Active Learning for Convolutional Neural Networks: A Core-Set Approach☆274Updated 6 years ago
- Training Confidence-Calibrated Classifier for Detecting Out-of-Distribution Samples / ICLR 2018☆182Updated 5 years ago
- A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks☆231Updated 6 years ago
- ICML'19: How does Disagreement Help Generalization against Label Corruption?☆21Updated 6 years ago
- TRAINING DEEP NEURAL-NETWORKS USING A NOISE ADAPTATION LAYER☆118Updated 8 years ago
- Code and website for DAL (Discriminative Active Learning) - a new active learning algorithm for neural networks in the batch setting. For…☆203Updated 5 years ago
- Pytorch implementation of the paper Bayesian Generative Active Deep Learning (ICML 2019).☆24Updated 6 years ago
- Learning Confidence for Out-of-Distribution Detection in Neural Networks☆275Updated 7 years ago
- Code for NeurIPS 2019 Paper, "L_DMI: An Information-theoretic Noise-robust Loss Function"☆119Updated 2 years ago
- Gold Loss Correction☆87Updated 6 years ago
- Variational Adversarial Active Learning (ICCV 2019)☆231Updated 2 years ago
- Code for the paper "A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks".☆347Updated 6 years ago
- Pytorch implementation of Virtual Adversarial Training☆134Updated 6 years ago
- Implementation of Temporal Ensembling for Semi-Supervised Learning by Laine et al. with tensorflow eager execution☆55Updated 5 years ago
- The Ultimate Reference for Out of Distribution Detection with Deep Neural Networks☆118Updated 5 years ago
- Reimplementation of "Realistic Evaluation of Deep Semi-Supervised Learning Algorithms"☆80Updated 5 years ago
- ICML'19 How does Disagreement Help Generalization against Label Corruption?☆88Updated 6 years ago
- Self-Supervised Learning for OOD Detection (NeurIPS 2019)☆267Updated 4 years ago
- Improving Consistency-Based Semi-Supervised Learning with Weight Averaging☆186Updated 6 years ago
- PyTorch Implementation of the paper Learning to Reweight Examples for Robust Deep Learning☆354Updated 6 years ago
- Code for the paper "Generalizing to Unseen Domains via Adversarial Data Augmentation", NeurIPS 2018☆121Updated 5 years ago
- Official implementation of "Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning"☆155Updated 4 years ago
- Virtual Adversarial Training (VAT) implementation for PyTorch☆295Updated 6 years ago
- Official Implementation of ICML 2019 Unsupervised label noise modeling and loss correction☆224Updated 5 years ago
- Code for paper "Learning to Reweight Examples for Robust Deep Learning"☆269Updated 6 years ago
- Temporal ensembling for semi-supervised learning☆159Updated 8 years ago
- PyTorch implementation of Temporal Ensembling for Semi-Supervised Learning☆111Updated 6 years ago
- Meta-Learning based Noise-Tolerant Training☆126Updated 5 years ago
- PyTorch implementation of Probabilistic End-to-end Noise Correction for Learning with Noisy Labels, CVPR 2019.☆139Updated 6 years ago
- ICML 2019: Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels☆91Updated 4 years ago