ozansener / active_learning_coresetLinks
Source code for ICLR 2018 Paper: Active Learning for Convolutional Neural Networks: A Core-Set Approach
☆276Updated 7 years ago
Alternatives and similar repositories for active_learning_coreset
Users that are interested in active_learning_coreset are comparing it to the libraries listed below
Sorting:
- Variational Adversarial Active Learning (ICCV 2019)☆231Updated 2 years ago
- Code and website for DAL (Discriminative Active Learning) - a new active learning algorithm for neural networks in the batch setting. For…☆203Updated 6 years ago
- Reproducing experimental results of LL4AL [Yoo et al. 2019 CVPR]☆223Updated 5 years ago
- An implementation of the BADGE batch active learning algorithm.☆209Updated last year
- Awesome Active Learning Paper List☆143Updated last year
- Deep Active Learning☆841Updated 3 years ago
- Code for the paper "A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks".☆349Updated 6 years ago
- PyTorch implementation of the paper "Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels" in NIPS 2018☆130Updated 6 years ago
- A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks☆233Updated 6 years ago
- Self-Supervised Learning for OOD Detection (NeurIPS 2019)☆268Updated 4 years ago
- Learning Confidence for Out-of-Distribution Detection in Neural Networks☆276Updated 7 years ago
- NeurIPS'19: Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting (Pytorch implementation for noisy labels).☆290Updated 3 years ago
- Code for paper: DivideMix: Learning with Noisy Labels as Semi-supervised Learning☆573Updated 5 years ago
- NeurIPS'18: Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels☆517Updated 4 years ago
- Training Confidence-Calibrated Classifier for Detecting Out-of-Distribution Samples / ICLR 2018☆182Updated 5 years ago
- Official Implementation of Early-Learning Regularization Prevents Memorization of Noisy Labels☆299Updated 2 years ago
- [NeurIPS 2019] Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss☆693Updated 3 years ago
- Adversarial Active Learning for Deep Networks☆14Updated 7 years ago
- PyTorch Implementation of the paper Learning to Reweight Examples for Robust Deep Learning☆354Updated 6 years ago
- Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning.☆246Updated last year
- PyTorch implementation of Probabilistic End-to-end Noise Correction for Learning with Noisy Labels, CVPR 2019.☆140Updated 6 years ago
- Official implementation of "Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning"☆155Updated 5 years ago
- Code for reproducing Manifold Mixup results (ICML 2019)☆495Updated last year
- [ICML2020] Normalized Loss Functions for Deep Learning with Noisy Labels☆141Updated last year
- Official Implementation of ICML 2019 Unsupervised label noise modeling and loss correction☆224Updated 5 years ago
- ☆130Updated 3 years ago
- Reimplementation of "Realistic Evaluation of Deep Semi-Supervised Learning Algorithms"☆80Updated 5 years ago
- PyTorch implementation of consistency regularization methods for semi-supervised learning☆79Updated 5 years ago
- The implementation of the algorithm in the paper "Safe Deep Semi-Supervised Learning for Unseen-Class Unlabeled Data" published in ICML 2…☆50Updated 5 years ago
- ☆428Updated 4 years ago