Neural Weight Search for Scalable Task Incremental Learning

• URL: http://arxiv.org/abs/2211.13823v1
• Date: Thu, 24 Nov 2022 23:30:23 GMT
• Title: Neural Weight Search for Scalable Task Incremental Learning
• Authors: Jian Jiang, Oya Celiktutan
• Abstract summary: 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 individual network or sub-network for future tasks. This leads to an ever-growing memory due to saving extra weights for new tasks and how to address this issue has remained an open problem in task incremental learning.
• Score: 6.413209417643468
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 individual 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 remained 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, resulting in scalable and controllable memory growth. Extensive 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.

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