# Lifelong Generative Modeling

## TL;DR

This paper introduces a lifelong learning approach for unsupervised generative modeling using a student-teacher VAE architecture that preserves learned distributions without storing past data, effectively reducing catastrophic interference.

## Contribution

It proposes a novel cross-model regularizer inspired by Bayesian updates, enabling continual learning of multiple distributions with a single model.

## Key findings

- Successfully mitigates catastrophic interference in sequential tasks
- Performs well on sequential MNIST, FashionMNIST, SVHN, and Celeb-A datasets
- Maintains learned distributions without storing past data

## Abstract

Lifelong learning is the problem of learning multiple consecutive tasks in a sequential manner, where knowledge gained from previous tasks is retained and used to aid future learning over the lifetime of the learner. It is essential towards the development of intelligent machines that can adapt to their surroundings. In this work we focus on a lifelong learning approach to unsupervised generative modeling, where we continuously incorporate newly observed distributions into a learned model. We do so through a student-teacher Variational Autoencoder architecture which allows us to learn and preserve all the distributions seen so far, without the need to retain the past data nor the past models. Through the introduction of a novel cross-model regularizer, inspired by a Bayesian update rule, the student model leverages the information learned by the teacher, which acts as a probabilistic knowledge store. The regularizer reduces the effect of catastrophic interference that appears when we learn over sequences of distributions. We validate our model's performance on sequential variants of MNIST, FashionMNIST, PermutedMNIST, SVHN and Celeb-A and demonstrate that our model mitigates the effects of catastrophic interference faced by neural networks in sequential learning scenarios.