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
• sungyubkim / MCDO
  A pytorch implementation of MCDO(Monte-Carlo Dropout methods)
  ☆56Updated 6 years ago
• DenisDsh / PyTorch-Deep-CORAL
  This is a PyTorch implementation of the Unsupervised Domain Adaptation method proposed in the paper Deep CORAL: Correlation Alignment for…
  ☆55Updated 6 years ago
• lunayht / DBALwithImgData
  Deep Bayesian Active Learning with Image Data by Gal et al. (ICML 2017)
  ☆44Updated 3 years ago
• acl21 / deep-active-learning-pytorch
  A PyTorch toolkit with 8 popular deep active learning query methods implemented.
  ☆88Updated 3 years ago
• cure-lab / deep-active-learning
  An implementation of the state-of-the-art Deep Active Learning algorithms
  ☆103Updated last year
• mlaves / bayesian-temperature-scaling
  Code for "Well-calibrated Model Uncertainty with Temperature Scaling for Dropout Variational Inference" (NeurIPS Bayesian Deep Learning W…
  ☆24Updated 5 years ago
• PrateekMunjal / TorchAL
  Official implementation of our paper: Towards Robust and Reproducible Active Learning using Neural Networks, accepted at CVPR 2022.
  ☆67Updated last year
• jayheo / UA
  ☆24Updated 6 years ago
• anirudhshenoy / pseudo_labeling_small_datasets
  Pseudo Labeling for Neural Networks and Logistic Regression/SVMs ( Based on "Pseudo-Label : The Simple and Efficient Semi-Supervised Lear…
  ☆73Updated 5 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
• EricArazo / PseudoLabeling
  Official implementation of "Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning"
  ☆153Updated 4 years ago
• neuropoly / deep-active-learning
  Implementation of active learning features for AxonDeepSeg software (axon-myelin segmentation)
  ☆38Updated 6 years ago
• cjshui / WAAL
  Wasserstein Adversarial Active Learning
  ☆29Updated 5 years ago
• michaal94 / Semisupervised-Clustering
  PyTorch semi-supervised clustering with Convolutional Autoencoders
  ☆24Updated 6 years ago
• damienlancry / DBAL
  Implementation of Deep Bayesian Active Learning with Image Data with modAL (python module for Active Learning)
  ☆26Updated 6 years ago
• ptrckhmmr / DEAL
  Code for "Deal: Deep Evidential Active Learning for Image Classification" (ICMLA 2020)
  ☆21Updated 4 years ago
• ykwon0407 / UQ_BNN
  Uncertainty quantification using Bayesian neural networks in classification (MIDL 2018, CSDA)
  ☆137Updated 5 years ago
• amrit110 / Active-Learning-Papers
  A list of papers on Active Learning and Uncertainty Estimation for Neural Networks.
  ☆66Updated 5 years ago
• atilberk / evidential-deep-learning-to-quantify-classification-uncertainty
  Work on Evidential Deep Learning to Quantify Classification Uncertainty
  ☆60Updated 6 years ago
• mlaves / well-calibrated-regression-uncertainty
  Code for our MIDL2020 submission "Well-Calibrated Regression Uncertainty in Medical Imaging with Deep Learning".
  ☆31Updated 4 years ago
• DelTA-Lab-IITK / U-CAM
  Visual Explanation using Uncertainty based Class Activation Maps
  ☆23Updated 5 years ago
• aredier / monte_carlo_dropout
  using monte carlo dropout to have uncertainty estimation of predictions
  ☆14Updated 5 years ago
• seominseok0429 / Learning-Loss-for-Active-Learning-Pytorch
  Learning Loss for Active Learning Pytorch Implementation,(reproduction)
  ☆32Updated 5 years ago
• huyng / incertae
  Code for MC Dropout and Model Ensembling Uncertainty Estimate experiments
  ☆68Updated 5 years ago
• agaldran / cost_sensitive_loss_classification
  A straightforward mechanism to implement cost sensitive losses in pytorch
  ☆47Updated 3 years ago
• shibuiwilliam / ActiveSemiSupervisedDeepLearning
  Comparing active learning, semi-supervised learning and combination of them on deep learning.
  ☆14Updated 7 years ago
• divamgupta / deep-clustering-kingdra
  Official implementation of ICLR 2020 paper Unsupervised Clustering using Pseudo-semi-supervised Learning
  ☆47Updated 4 years ago
• stanfordmlgroup / MedSelect
  Learns effective selective labeling strategies for medical images using deep reinforcement learning and meta learning
  ☆26Updated 4 years ago
• cpark321 / uncertainty-deep-learning
  ☆238Updated 5 years ago