Incremental Learning of Structured Memory via Closed-Loop Transcription

• URL: http://arxiv.org/abs/2202.05411v3
• Date: Wed, 7 Jun 2023 05:00:32 GMT
• Title: Incremental Learning of Structured Memory via Closed-Loop Transcription
• Authors: Shengbang Tong, Xili Dai, Ziyang Wu, Mingyang Li, Brent Yi, Yi Ma
• Abstract summary: This work proposes a minimal computational model for learning structured memories of multiple object classes in an incremental setting. Our method is simpler than existing approaches for incremental learning, and more efficient in terms of model size, storage, and computation. Experimental results show that our method can effectively alleviate catastrophic forgetting, achieving significantly better performance than prior work of generative replay.
• Score: 20.255633973040183
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This work proposes a minimal computational model for learning structured memories of multiple object classes in an incremental setting. Our approach is based on establishing a closed-loop transcription between the classes and a corresponding set of subspaces, known as a linear discriminative representation, in a low-dimensional feature space. Our method is simpler than existing approaches for incremental learning, and more efficient in terms of model size, storage, and computation: it requires only a single, fixed-capacity autoencoding network with a feature space that is used for both discriminative and generative purposes. Network parameters are optimized simultaneously without architectural manipulations, by solving a constrained minimax game between the encoding and decoding maps over a single rate reduction-based objective. Experimental results show that our method can effectively alleviate catastrophic forgetting, achieving significantly better performance than prior work of generative replay on MNIST, CIFAR-10, and ImageNet-50, despite requiring fewer resources. Source code can be found at https://github.com/tsb0601/i-CTRL

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