Skip to yearly menu bar Skip to main content


Overcoming Multi-model Forgetting

Yassine Benyahia · Kaicheng Yu · Kamil Bennani-Smires · Martin Jaggi · Anthony C. Davison · Mathieu Salzmann · Claudiu Musat

Pacific Ballroom #19

Keywords: [ Optimization ] [ Bayesian Methods ] [ Architectures ]


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.

Live content is unavailable. Log in and register to view live content