Overcoming Multi-model Forgetting

Yassine Benyahia*, Kaicheng Yu*, Kamil Bennani-Smires, Martin Jaggi, Anthony Davison, Mathieu Salzmann, Claudiu Musat

Abstract: We identify a phenomenon, which we refer to as *multi-model forgetting*, that occurs when sequentially training multiple deep networks with partially-shared parameters; the performance of previously-trained models degrades as one optimizes a subsequent one, due to the overwriting of shared parameters. To overcome this, we introduce a statistically-justified weight plasticity loss that regularizes the learning of a model's shared parameters according to their importance for the previous models, and demonstrate its effectiveness when training two models sequentially and for neural architecture search. Adding weight plasticity in neural architecture search preserves the best models to the end of the search and yields improved results in both natural language processing and computer vision tasks.

Keywords: multi-model forgetting, deep learning, machine learning, multi-model training, neural architecture search

TL;DR: We identify a phenomenon, neural brainwashing, and introduce a statistically-justified weight plasticity loss to overcome this.