{"id":13712524,"url":"https://github.com/ej0cl6/deep-active-learning","last_synced_at":"2025-05-06T22:31:15.812Z","repository":{"id":38361451,"uuid":"131947745","full_name":"ej0cl6/deep-active-learning","owner":"ej0cl6","description":"Deep Active Learning","archived":false,"fork":false,"pushed_at":"2022-10-03T20:23:38.000Z","size":41,"stargazers_count":809,"open_issues_count":0,"forks_count":184,"subscribers_count":15,"default_branch":"master","last_synced_at":"2024-11-13T23:32:16.689Z","etag":null,"topics":["active-learning","deep-active-learning"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/ej0cl6.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2018-05-03T05:51:17.000Z","updated_at":"2024-11-13T16:38:29.000Z","dependencies_parsed_at":"2022-07-12T17:27:45.518Z","dependency_job_id":null,"html_url":"https://github.com/ej0cl6/deep-active-learning","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ej0cl6%2Fdeep-active-learning","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ej0cl6%2Fdeep-active-learning/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ej0cl6%2Fdeep-active-learning/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ej0cl6%2Fdeep-active-learning/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/ej0cl6","download_url":"https://codeload.github.com/ej0cl6/deep-active-learning/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":252779012,"owners_count":21802865,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["active-learning","deep-active-learning"],"created_at":"2024-08-02T23:01:19.425Z","updated_at":"2025-05-06T22:31:15.527Z","avatar_url":"https://github.com/ej0cl6.png","language":"Python","readme":"# DeepAL: Deep Active Learning in Python\n\nPython implementations of the following active learning algorithms:\n\n- Random Sampling\n- Least Confidence [1]\n- Margin Sampling [2]\n- Entropy Sampling [3]\n- Uncertainty Sampling with Dropout Estimation [4]\n- Bayesian Active Learning Disagreement [4]\n- Cluster-Based Selection [5]\n- Adversarial margin [6]\n\n## Prerequisites \n\n- numpy            1.21.2\n- scipy            1.7.1\n- pytorch          1.10.0\n- torchvision      0.11.1\n- scikit-learn     1.0.1\n- tqdm             4.62.3\n- ipdb             0.13.9\n\nYou can also use the following command to install conda environment\n\n```\nconda env create -f environment.yml\n```\n\n## Demo \n\n```\n  python demo.py \\\n      --n_round 10 \\\n      --n_query 1000 \\\n      --n_init_labeled 10000 \\\n      --dataset_name MNIST \\\n      --strategy_name RandomSampling \\\n      --seed 1\n```\n\nPlease refer [here](https://arxiv.org/abs/2111.15258) for more details.\n\n## Citing\n\nIf you use our code in your research or applications, please consider citing our paper.\n\n```\n@article{Huang2021deepal,\n    author    = {Kuan-Hao Huang},\n    title     = {DeepAL: Deep Active Learning in Python},\n    journal   = {arXiv preprint arXiv:2111.15258},\n    year      = {2021},\n}\n```\n\n## Reference\n\n[1] A Sequential Algorithm for Training Text Classifiers, SIGIR, 1994\n\n[2] Active Hidden Markov Models for Information Extraction, IDA, 2001\n\n[3] Active learning literature survey. University of Wisconsin-Madison Department of Computer Sciences, 2009\n\n[4] Deep Bayesian Active Learning with Image Data, ICML, 2017\n\n[5] Active Learning for Convolutional Neural Networks: A Core-Set Approach, ICLR, 2018\n\n[6] Adversarial Active Learning for Deep Networks: a Margin Based Approach, arXiv, 2018\n\n\n\n\n\n\n","funding_links":[],"categories":["scientific publications","Table of Contents","Python","3.3 AL in AI Fields - 人工智能背景中的主动学习"],"sub_categories":["Sampling as a step of the publication","**Tutorials - 教程**"],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fej0cl6%2Fdeep-active-learning","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fej0cl6%2Fdeep-active-learning","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fej0cl6%2Fdeep-active-learning/lists"}