BooVAE: Boosting Approach for Continual Learning of VAE

By Anna Kuzina, Evgenii Egorov, Evgeny Burnaev. 2019

34 citations recorded by the source corpus.

Variational autoencoder (VAE) is a deep generative model for unsupervised learning, allowing to encode observations into the meaningful latent space. VAE is prone to catastrophic forgetting when tasks arrive sequentially, and only the data for the current one is available. We address this problem of continual learning for VAEs. It is known that the choice of the prior distribution over the latent space is crucial for VAE in the non-continual setting. We argue that it can also be helpful to avoid catastrophic forgetting. We learn the approximation of the aggregated posterior as a prior for each task. This approximation is parametrised as an additive mixture of distributions induced by encoder evaluated at trainable pseudo-inputs. We use a greedy boosting-like approach with entropy regularisation to learn the components. This method encourages components diversity, which is essential as we aim at memorising the current task with the fewest components possible. Based on the learnable prior, we introduce an end-to-end approach for continual learning of VAEs and provide empirical studies on commonly used benchmarks (MNIST, Fashion MNIST, NotMNIST) and CelebA datasets. For each dataset, the proposed method avoids catastrophic forgetting in a fully automatic way.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Generative Models, Meta & Continual Learning

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Citations in the corpus

A Comprehensive Survey of Continual Learning: Theory, Method and Application (2023, background): “BooVAE [110] instead adopts a static architecture through incorporating each”
How to Reuse and Compose Knowledge for a Lifetime of Tasks: A Survey on Continual Learning and Functional Composition (2022, methodology): “Whereas these methods have operated in the supervised setting, Egorov et al. (2021) recently proposed a VAE model with boosting density approximation for the unsupervised setting.”
Continual Variational Autoencoder Learning via Online Cooperative Memorization (2022, methodology): “One of the most popular approaches is to use a regularization loss within the optimization procedure [14,21,23,26,36,41,47,52,56], where the network parameters which are important to the past learnt data are re-weighted when learning a new task, in order to attempt to preserve past knowledge.”