A Comparative Study of Continual, Lifelong, and Online Supervised Learning Libraries


  • Logan Cummins Mississippi State University https://orcid.org/0000-0002-3875-4137
  • Brad Killen Mississippi State University
  • Somayeh Bakhtiari Ramezani Mississippi State University
  • Shahram Rahimi Mississippi State University
  • Maria Seale U.S. Army Eng. Research and Dev. Center
  • Sudip Mittal Mississippi State University




online learning, continual learning, lifelong learning, classification


Machine learning has shown to be a crucial part of big data analytics; however, it lacks when the data is continuously streaming in from the system and changing too much from the original training data. Online learning is machine learning for streaming data that arrives in a sequential order where the model updates after every data point. While machine learning relies on well-established libraries such as PyTorch and Keras, the libraries for online learning are less well known, but they are here to serve similar purposes of reproducibility and reducing the time from research to production. Here, we compare different libraries for online learning research, specifically supervised learning. We compare them on the axes of developmental experience and benchmark testing as researchers. Our comparison as developers takes maintenance, documentation, and offerings of state-of-the-art algorithms into account. As this is not necessarily free of bias, we also use benchmarks known to online learning to gather power usage, RAM usage, speed, and accuracy of these libraries to get an objective view. Our findings show that Avalanche and River, including River-torch, are among the best libraries in terms of performance and applicability to the research in supervised online learning.




How to Cite

Cummins, N., Killen, B., Ramezani, S. B., Rahimi, S., Seale, M., & Mittal, S. . (2023). A Comparative Study of Continual, Lifelong, and Online Supervised Learning Libraries. The International FLAIRS Conference Proceedings, 36(1). https://doi.org/10.32473/flairs.36.133171



Main Track Proceedings