Composing without forgetting

In this project, we propose a continual learning approach to face the problem of catastrophic forgetting in online image classification problems. Concretely, we propose a model that learns how to mask a series of general modules in a deep learning architecture, so that generalization emerges through the composition of those modules. This is of vital importance for Element AI to provide reusable solutions that scale with new data, without the need of learning a new model for every problem and improving the overall performance.

Faculty Supervisor:

Laurent Charlin

Student:

Massimo Caccia

Partner:

Element AI

Discipline:

Computer science

Sector:

Information and communications technologies

University:

HEC Montréal

Program:

Accelerate

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