Neural Weight Search for Scalable Task Incremental Learning

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Task incremental learning aims to enable a system to maintain its performance on previously learned tasks while learning new tasks, solving the problem of catastrophic forgetting. One promising approach is to build an indi- vidual network or sub-network for future tasks. However, this leads to an ever-growing memory due to saving extra weights for new tasks and how to address this issue has re- mained an open problem in task incremental learning. In this paper, we introduce a novel Neural Weight Search technique that designs a fixed search space where the optimal combinations of frozen weights can be searched to build new models for novel tasks in an end-to-end manner, result- ing in a scalable and controllable memory growth. Exten- sive experiments on two benchmarks, i.e., Split-CIFAR-100 and CUB-to-Sketches, show our method achieves state-of- the-art performance with respect to both average inference accuracy and total memory cost.
Original languageEnglish
Title of host publicationIEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
Publication statusAccepted/In press - 2023


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