A SOM-VAE generative replay method stores per-unit Gaussian statistics instead of raw data and reports competitive class-incremental accuracy on standard benchmarks.
Continual Learning of Context-dependent Processing in Neural Networks
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abstract
Deep neural networks (DNNs) are powerful tools in learning sophisticated but fixed mapping rules between inputs and outputs, thereby limiting their application in more complex and dynamic situations in which the mapping rules are not kept the same but changing according to different contexts. To lift such limits, we developed a novel approach involving a learning algorithm, called orthogonal weights modification (OWM), with the addition of a context-dependent processing (CDP) module. We demonstrated that with OWM to overcome the problem of catastrophic forgetting, and the CDP module to learn how to reuse a feature representation and a classifier for different contexts, a single network can acquire numerous context-dependent mapping rules in an online and continual manner, with as few as $\sim$10 samples to learn each. This should enable highly compact systems to gradually learn myriad regularities of the real world and eventually behave appropriately within it.
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cs.LG 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
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Class Incremental Continual Learning with Self-Organizing Maps and Variational Autoencoders Using Synthetic Replay
A SOM-VAE generative replay method stores per-unit Gaussian statistics instead of raw data and reports competitive class-incremental accuracy on standard benchmarks.