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In this work, we introduce Adapt & Align, a method for continual learning ofneural networks by aligning latent representations in generative models. NeuralNetworks suffer from abrupt loss in performance when retrained with additionaltraining data from different distributions. At the same time, training withadditional data without access to the previous examples rarely improves themodel's performance. In this work, we propose a new method that mitigates thoseproblems by employing generative models and splitting the process of theirupdate into two parts. In the first one, we train a local generative modelusing only data from a new task. In the second phase, we consolidate latentrepresentations from the local model with a global one that encodes knowledgeof all past experiences. We introduce our approach with VariationalAuteoncoders and Generative Adversarial Networks. Moreover, we show how we canuse those generative models as a general method for continual knowledgeconsolidation that can be used in downstream tasks such as classification.

Adapt & Align: Continual Learning with Generative Models Latent Space Alignment