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arXiv · 2306.06545

A Probabilistic Framework for Modular Continual Learning

Lazar Valkov·Akash Srivastava·Swarat Chaudhuri·Charles Sutton
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Abstract

Modular approaches that use a different composition of modules for each problem are a promising direction in continual learning (CL). However, searching through the large, discrete space of module compositions is challenging, especially because evaluating a composition's performance requires a round of neural network training. We address this challenge through a modular CL framework, PICLE, that uses a probabilistic model to cheaply compute the fitness of each composition, allowing PICLE to achieve both perceptual, few-shot and latent transfer. The model combines prior knowledge about good module compositions with dataset-specific information. We evaluate PICLE using two benchmark suites designed to assess different desiderata of CL techniques. Comparing to a wide range of approaches, we show that PICLE is the first modular CL algorithm to achieve perceptual, few-shot and latent transfer while scaling well to large search spaces, outperforming previous state-of-the-art modular CL approaches on long problem sequences.

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Lazar Valkov, Akash Srivastava, Swarat Chaudhuri, Charles Sutton. 2023-06-11. A Probabilistic Framework for Modular Continual Learning. https://arxiv.org/abs/2306.06545

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