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Visual recognition algorithms are required today to exhibit adaptiveabilities. Given a deep model trained on a specific, given task, it would behighly desirable to be able to adapt incrementally to new tasks, preservingscalability as the number of new tasks increases, while at the same timeavoiding catastrophic forgetting issues. Recent work has shown that masking theinternal weights of a given original conv-net through learned binary variablesis a promising strategy. We build upon this intuition and take into accountmore elaborated affine transformations of the convolutional weights thatinclude learned binary masks. We show that with our generalization it ispossible to achieve significantly higher levels of adaptation to new tasks,enabling the approach to compete with fine tuning strategies by requiringslightly more than 1 bit per network parameter per additional task. Experimentson two popular benchmarks showcase the power of our approach, that achieves thenew state of the art on the Visual Decathlon Challenge.

Adding New Tasks to a Single Network with Weight Transformations using Binary Masks