[ContinualAI Reading group]: "Class-Incremental Learning with Generative Classifiers"

[Keiland Cooper]

Hi All,

This Friday 21-05-2021, 5.30pm CEST, for the ContinualAI Reading Group, Gido M. van de Ven  will present the paper:

Title: “Class-Incremental Learning with Generative Classifiers” 

Abstract: Incrementally training deep neural networks to recognize new classes is a challenging problem. Most existing class-incremental learning methods store data or use generative replay, both of which have drawbacks, while 'rehearsal-free' alternatives such as parameter regularization or bias-correction methods do not consistently achieve high performance. Here, we put forward a new strategy for class-incremental learning: generative classification. Rather than directly learning the conditional distribution p(y|x), our proposal is to learn the joint distribution p(x,y), factorized as p(x|y)p(y), and to perform classification using Bayes' rule. As a proof-of-principle, here we implement this strategy by training a variational autoencoder for each class to be learned and by using importance sampling to estimate the likelihoods p(x|y). This simple approach performs very well on a diverse set of continual learning benchmarks, outperforming generative replay and other existing baselines that do not store data.

The event will be moderated by: Vincenzo Lomonaco

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Looking forward to seeing you all there!

All the best,

Keiland Cooper

University of California

ContinualAI Co-founding Board Member

http://kwcooper.xyz