Multiband VAE: Latent Space Partitioning for Knowledge Consolidation in Continual Learning

• URL: http://arxiv.org/abs/2106.12196v1
• Date: Wed, 23 Jun 2021 06:58:40 GMT
• Title: Multiband VAE: Latent Space Partitioning for Knowledge Consolidation in Continual Learning
• Authors: Kamil Deja, Pawe{\l} Wawrzy\'nski, Daniel Marczak, Wojciech Masarczyk, Tomasz Trzci\'nski
• Abstract summary: Acquiring knowledge about new data samples without forgetting previous ones is a critical problem of continual learning. We propose a new method for unsupervised continual knowledge consolidation in generative models that relies on the partitioning of Variational Autoencoder's latent space. On top of the standard continual learning evaluation benchmarks, we evaluate our method on a new knowledge consolidation scenario and show that the proposed approach outperforms state-of-the-art by up to twofold.
• Score: 14.226973149346883
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a new method for unsupervised continual knowledge consolidation in generative models that relies on the partitioning of Variational Autoencoder's latent space. Acquiring knowledge about new data samples without forgetting previous ones is a critical problem of continual learning. Currently proposed methods achieve this goal by extending the existing model while constraining its behavior not to degrade on the past data, which does not exploit the full potential of relations within the entire training dataset. In this work, we identify this limitation and posit the goal of continual learning as a knowledge accumulation task. We solve it by continuously re-aligning latent space partitions that we call bands which are representations of samples seen in different tasks, driven by the similarity of the information they contain. In addition, we introduce a simple yet effective method for controlled forgetting of past data that improves the quality of reconstructions encoded in latent bands and a latent space disentanglement technique that improves knowledge consolidation. On top of the standard continual learning evaluation benchmarks, we evaluate our method on a new knowledge consolidation scenario and show that the proposed approach outperforms state-of-the-art by up to twofold across all testing scenarios.

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