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Status: Unchecked ×Keyword: incremental learning ×Clear all

1 claim from 1 paper

1. Computer Science › Domain Adaptation and Few-Shot Learning

   Reparameterizing Convolutions for Incremental Multi-Task Learning Without Task Interference

   Kanakis, Brüggemann, Saha, Georgoulis, Obukhov and Van Gool · Lecture notes in computer science · 2020

   The paper reparameterizes convolutions into a shared non-trainable filter bank and task-specific modulators, addressing incremental learning and task interference, and reports state-of-the-art results on two benchmarks.

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   1. UncheckedSplitting convolutions into a fixed shared filter bank plus task-specific modulators lets a network learn new tasks without harming existing ones.“Thus, our reparameterization enables the model to learn new tasks without adversely affecting the performance of existing ones.”