A Probabilistic Framework for Modular Continual Learning
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Keywords: continual learning, modular machine learning, modular continual learning, transfer learning, catastrophic forgetting, Bayesian optimization, probabilistic modelling
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TL;DR: We use probabilistic modelling over pre-trained modules and the data to speed up modular continual learning.
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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Primary Area: transfer learning, meta learning, and lifelong learning
Submission Number: 6487
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