Alternatives and similar repositories for CEAL

Users that are interested in CEAL are comparing it to the libraries listed below

• osimeoni / RethinkingDeepActiveLearning
  ☆15Updated 4 years ago
• ptrckhmmr / DEAL
  Code for "Deal: Deep Evidential Active Learning for Image Classification" (ICMLA 2020)
  ☆21Updated 4 years ago
• avihu111 / TypiClust
  Active Learning on a Budget - Opposite Strategies Suit High and Low Budgets
  ☆99Updated last year
• acl21 / deep-active-learning-pytorch
  A PyTorch toolkit with 8 popular deep active learning query methods implemented.
  ☆89Updated 4 years ago
• bbuing9 / DARP
  Code for the paper "Distribution Aligning Refinery of Pseudo-label for Imbalanced Semi-supervised Learning" (NeurIPS 20)
  ☆75Updated 3 years ago
• lim3944 / ReMixmatch-pytorch
  ☆14Updated 5 years ago
• PrateekMunjal / TorchAL
  Official implementation of our paper: Towards Robust and Reproducible Active Learning using Neural Networks, accepted at CVPR 2022.
  ☆68Updated 2 years ago
• dbbbbm / UAE
  Implementation of "Abnormality Detection in Chest X-ray Images Using Uncertainty Prediction Autoencoders" in MICCAI 2020.
  ☆20Updated 5 years ago
• sungyubkim / MCDO
  A pytorch implementation of MCDO(Monte-Carlo Dropout methods)
  ☆57Updated 6 years ago
• ShellingFord221 / My-implementation-of-What-Uncertainties-Do-We-Need-in-Bayesian-Deep-Learning-for-Computer-Vision
  Pytorch implementation of classification task in What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision (simple vers…
  ☆79Updated 4 years ago
• cure-lab / deep-active-learning
  An implementation of the state-of-the-art Deep Active Learning algorithms
  ☆106Updated 2 years ago
• lunayht / DBALwithImgData
  Deep Bayesian Active Learning with Image Data by Gal et al. (ICML 2017)
  ☆45Updated 3 years ago
• aredier / monte_carlo_dropout
  using monte carlo dropout to have uncertainty estimation of predictions
  ☆16Updated 6 years ago
• Mephisto405 / Learning-Loss-for-Active-Learning
  Reproducing experimental results of LL4AL [Yoo et al. 2019 CVPR]
  ☆223Updated 5 years ago
• agaldran / balanced_mixup
  Repository for MICCAI 2021 paper Balanced-MixUp for Highly Imbalanced Medical Image Classification
  ☆73Updated 3 years ago
• svdesai / coreset-al
  A greedy implementation of coreset based active learning for image classification (https://arxiv.org/abs/1708.00489)
  ☆23Updated 4 years ago
• sinhasam / vaal
  Variational Adversarial Active Learning (ICCV 2019)
  ☆231Updated 2 years ago
• SineZHAN / deepALplus
  This is a toolbox for Deep Active Learning, an extension from previous work https://github.com/ej0cl6/deep-active-learning (DeepAL toolbo…
  ☆183Updated last year
• muratsensoy / muratsensoy.github.io
  ☆12Updated 4 years ago
• lyn1874 / region_based_active_learning
  On uncertainty estimation in active learning for image segmentation
  ☆12Updated 5 years ago
• cubeyoung / TA-VAAL
  [CVPR2021] Task-Aware Variational Adversarial Active Learning
  ☆42Updated last year
• hula-ai / skin_lesion_uncertainty_estimation
  Code and models for our paper "Risk-Aware Machine Learning Classifier for Skin Lesion Diagnosis"
  ☆10Updated last year
• siit-vtt / semi-supervised-learning-pytorch
  Several SSL methods (Pi model, Mean Teacher) are implemented in pytorch
  ☆83Updated 7 years ago
• kiraving / SegGradCAM
  SEG-GRAD-CAM: Interpretable Semantic Segmentation via Gradient-Weighted Class Activation Mapping
  ☆105Updated 2 years ago
• wyf0912 / LDDG
  [NIPS 2020] The code release of paper 'Domain Generalization for Medical Imaging Classification with Linear-Dependency Regularization'
  ☆66Updated 5 years ago
• cjshui / WAAL
  Wasserstein Adversarial Active Learning
  ☆29Updated 5 years ago
• euphoria0-0 / Learning-Loss-for-Active-Learning
  An unofficial PyTorch implementation of "Learning Loss for Active Learning"
  ☆10Updated 3 years ago
• johnsk95 / PT4AL
  Official PyTorch implementation of "PT4AL: Using Self-Supervised Pretext Tasks for Active Learning (ECCV2022)"
  ☆56Updated last year
• mlaves / well-calibrated-regression-uncertainty
  Code for our MIDL2020 submission "Well-Calibrated Regression Uncertainty in Medical Imaging with Deep Learning".
  ☆31Updated 4 years ago
• jayheo / UA
  ☆25Updated 6 years ago