Alternatives and similar repositories for deep-active-learning:

Users that are interested in deep-active-learning are comparing it to the libraries listed below

• rmunro / pytorch_active_learning
  PyTorch Library for Active Learning to accompany Human-in-the-Loop Machine Learning book
  ☆955Updated 2 years ago
• google / active-learning
  ☆1,137Updated 2 years ago
• NUAA-AL / ALiPy
  ALiPy: Active Learning in Python is an active learning python toolbox, which allows users to conveniently evaluate, compare and analyze t…
  ☆873Updated 2 years ago
• baifanxxx / awesome-active-learning
  A curated list of awesome Active Learning
  ☆754Updated 3 months ago
• ozansener / active_learning_coreset
  Source code for ICLR 2018 Paper: Active Learning for Convolutional Neural Networks: A Core-Set Approach
  ☆265Updated 6 years ago
• dsgissin / DiscriminativeActiveLearning
  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
• SineZHAN / deepALplus
  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
• JordanAsh / badge
  An implementation of the BADGE batch active learning algorithm.
  ☆202Updated 8 months ago
• BlackHC / BatchBALD
  Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning.
  ☆235Updated 8 months ago
• SupeRuier / awesome-active-learning
  Everything you need about Active Learning (AL).
  ☆849Updated 8 months ago
• sinhasam / vaal
  Variational Adversarial Active Learning (ICCV 2019)
  ☆225Updated last year
• Mephisto405 / Learning-Loss-for-Active-Learning
  Reproducing experimental results of LL4AL [Yoo et al. 2019 CVPR]
  ☆218Updated 4 years ago
• yongjin-shin / awesome-active-learning
  Awesome Active Learning Paper List
  ☆142Updated 9 months ago
• modAL-python / modAL
  A modular active learning framework for Python
  ☆2,260Updated 11 months ago
• yassouali / awesome-semi-supervised-learning
  😎 An up-to-date & curated list of awesome semi-supervised learning papers, methods & resources.
  ☆1,813Updated 8 months ago
• google-research / fixmatch
  A simple method to perform semi-supervised learning with limited data.
  ☆1,122Updated 6 months ago
• baal-org / baal
  Bayesian active learning library for research and industrial usecases.
  ☆887Updated 7 months ago
• TorchSSL / TorchSSL
  A PyTorch-based library for semi-supervised learning (NeurIPS'21)
  ☆1,324Updated last year
• kekmodel / FixMatch-pytorch
  Unofficial PyTorch implementation of "FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence"
  ☆767Updated last year
• decile-team / distil
  DISTIL: Deep dIverSified inTeractIve Learning. An active/inter-active learning library built on py-torch for reducing labeling costs.
  ☆147Updated 2 years ago
• cure-lab / deep-active-learning
  An implementation of the state-of-the-art Deep Active Learning algorithms
  ☆102Updated last year
• YyzHarry / imbalanced-semi-self
  [NeurIPS 2020] Semi-Supervision (Unlabeled Data) & Self-Supervision Improve Class-Imbalanced / Long-Tailed Learning
  ☆742Updated 3 years ago
• LiJunnan1992 / DivideMix
  Code for paper: DivideMix: Learning with Noisy Labels as Semi-supervised Learning
  ☆550Updated 4 years ago
• YU1ut / MixMatch-pytorch
  Code for "MixMatch - A Holistic Approach to Semi-Supervised Learning"
  ☆642Updated last year
• songhwanjun / Awesome-Noisy-Labels
  A Survey
  ☆544Updated 2 years ago
• zzw-zwzhang / Awesome-of-Long-Tailed-Recognition
  A curated list of long-tailed recognition resources.
  ☆583Updated last year
• bhanML / Co-teaching
  NeurIPS'18: Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels
  ☆498Updated 3 years ago
• wetliu / energy_ood
  ☆406Updated 3 years ago
• kaidic / LDAM-DRW
  [NeurIPS 2019] Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss
  ☆659Updated 3 years ago
• vandit15 / Class-balanced-loss-pytorch
  Pytorch implementation of the paper "Class-Balanced Loss Based on Effective Number of Samples"
  ☆790Updated last year