ozansener / active_learning_coreset
Source code for ICLR 2018 Paper: Active Learning for Convolutional Neural Networks: A Core-Set Approach
☆265Updated 6 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
- Variational Adversarial Active Learning (ICCV 2019)☆225Updated last year
- 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
- Reproducing experimental results of LL4AL [Yoo et al. 2019 CVPR]☆217Updated 4 years ago
- An implementation of the BADGE batch active learning algorithm.☆202Updated 8 months ago
- Awesome Active Learning Paper List☆142Updated 9 months ago
- Code for the paper "A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks".☆343Updated 5 years ago
- NeurIPS'18: Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels☆498Updated 3 years ago
- This repo consists of collection of papers and repos on the topic of deep learning by noisy labels / label noise.☆234Updated 3 years ago
- Deep Active Learning☆819Updated 2 years ago
- Code for paper: DivideMix: Learning with Noisy Labels as Semi-supervised Learning☆550Updated 4 years ago
- [NeurIPS 2019] Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss☆658Updated 3 years ago
- Official Implementation of ICML 2019 Unsupervised label noise modeling and loss correction☆222Updated 4 years ago
- PyTorch Implementation of the paper Learning to Reweight Examples for Robust Deep Learning☆354Updated 5 years ago
- Active Learning on a Budget - Opposite Strategies Suit High and Low Budgets☆88Updated 3 months 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
- Pytorch implementation of the paper Bayesian Generative Active Deep Learning (ICML 2019).☆24Updated 5 years ago
- Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning.☆234Updated 8 months ago
- Training Confidence-Calibrated Classifier for Detecting Out-of-Distribution Samples / ICLR 2018☆178Updated 4 years ago
- Learning Confidence for Out-of-Distribution Detection in Neural Networks☆268Updated 6 years ago
- Official Implementation of Early-Learning Regularization Prevents Memorization of Noisy Labels☆293Updated last year
- Meta-Learning based Noise-Tolerant Training☆123Updated 4 years ago
- A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks☆225Updated 6 years ago
- A curated list of long-tailed recognition resources.☆584Updated last year
- PyTorch implementation of Probabilistic End-to-end Noise Correction for Learning with Noisy Labels, CVPR 2019.☆139Updated 5 years ago
- ☆130Updated 2 years ago
- Official implementation of "Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning"☆153Updated 4 years ago
- NeurIPS'19: Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting (Pytorch implementation for noisy labels).☆284Updated 3 years ago
- Improving Consistency-Based Semi-Supervised Learning with Weight Averaging☆185Updated 6 years ago
- This is a toolbox for Deep Active Learning, an extension from previous work https://github.com/ej0cl6/deep-active-learning (DeepAL toolbo…☆174Updated 9 months ago
- ☆91Updated 4 years ago