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Found 3 papers, 3 papers with code
A Comprehensive Empirical Evaluation on Online Continual Learning
Online continual learning aims to get closer to a live learning experience by learning directly on a stream of data with temporally shifting distribution and by storing a minimum amount of data from that stream.
Improving Online Continual Learning Performance and Stability with Temporal Ensembles
Neural networks are very effective when trained on large datasets for a large number of iterations.
On the importance of cross-task features for class-incremental learning
We also define a new forgetting measure for class-incremental learning, and see that forgetting is not the principal cause of low performance.
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