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Lifelong Twin Generative Adversarial Networks

2021-07-09 22:52:49
Fei Ye, Adrian G. Bors
arXiv_AI
         
Abstract

In this paper, we propose a new continuously learning generative model, called the Lifelong Twin Generative Adversarial Networks (LT-GANs). LT-GANs learns a sequence of tasks from several databases and its architecture consists of three components: two identical generators, namely the Teacher and Assistant, and one Discriminator. In order to allow for the LT-GANs to learn new concepts without forgetting, we introduce a new lifelong training approach, namely Lifelong Adversarial Knowledge Distillation (LAKD), which encourages the Teacher and Assistant to alternately teach each other, while learning a new database. This training approach favours transferring knowledge from a more knowledgeable player to another player which knows less information about a previously given task.

Abstract (translated)

URL

https://arxiv.org/abs/2107.04708

PDF

https://arxiv.org/pdf/2107.04708.pdf

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