mlsanityData to 14 Oct 2026

DeepAL: Deep Active Learning in Python

arXiv 2111.15258 ↗First posted 30 Nov 2021cs.LGcited by 13

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Abstract (arXiv)

We present DeepAL, a Python library that implements several common strategies for active learning, with a particular emphasis on deep active learning. DeepAL provides a simple and unified framework based on PyTorch that allows users to easily load custom datasets, build custom data handlers, and design custom strategies without much modification of codes. DeepAL is open-source on Github and welcome any contribution.

Key facts. Cited by 13 papers in the map (data through 14 Oct 2026). Builds on 5 of its 12 references that are in the map. First posted on arXiv 30 Nov 2021.

Builds on

5 of 12 references.

References · 5

  1. 2016Adversarial examples in the physical worldcited by 1,769
  2. 2015DeepFool: a simple and accurate method to fool deep neural networkscited by 1,318
  3. 2017Deep Bayesian Active Learning with Image Datacited by 819
  4. 2018Adversarial Active Learning for Deep Networks: a Margin Based Approachcited by 158
  5. 2017libact: Pool-based Active Learning in Pythoncited by 12

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 3; 2023: 5; 2024: 3; 2025: 1; 2026: 1.

13 papers.

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