DeepAL: Deep Active Learning in Python
This tool addresses the need for accessible and flexible active learning implementations for researchers and practitioners, but it is incremental as it builds on existing methods without introducing new algorithms.
The authors introduced DeepAL, a Python library that implements common active learning strategies with a focus on deep learning, providing a simple, unified PyTorch-based framework for easy customization and open-source collaboration.
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.