LibContinual: A Library for Realistic Continual Learning
Published:Dec 26, 2025 13:59
•1 min read
•ArXiv
Analysis
This paper introduces LibContinual, a library designed to address the fragmented research landscape in Continual Learning (CL). It aims to provide a unified framework for fair comparison and reproducible research by integrating various CL algorithms and standardizing evaluation protocols. The paper also critiques common assumptions in CL evaluation, highlighting the need for resource-aware and semantically robust strategies.
Key Takeaways
- •LibContinual is a comprehensive library for Continual Learning, offering a unified framework for research.
- •The paper identifies and critiques common assumptions in CL evaluation, highlighting their limitations.
- •The study emphasizes the need for resource-aware and semantically robust CL strategies.
- •The library is available on GitHub for public use and further research.
Reference
“The paper argues that common assumptions in CL evaluation (offline data accessibility, unregulated memory resources, and intra-task semantic homogeneity) often overestimate the real-world applicability of CL methods.”