SR-CIS: Self-Reflective Incremental System with Decoupled Memory and Reasoning

• URL: http://arxiv.org/abs/2408.01970v1
• Date: Sun, 4 Aug 2024 09:09:35 GMT
• Title: SR-CIS: Self-Reflective Incremental System with Decoupled Memory and Reasoning
• Authors: Biqing Qi, Junqi Gao, Xinquan Chen, Dong Li, Weinan Zhang, Bowen Zhou,
• Abstract summary: We propose the Self-Reflective Complementary Incremental System (SR-CIS) It consists of the Complementary Inference Module (CIM) and Complementary Memory Module (CMM) CMM consists of task-specific Short-Term Memory (STM) region and a universal Long-Term Memory (LTM) region. By storing textual descriptions of images during training and combining them with the Scenario Replay Module (SRM) post-training for memory combination, SR-CIS achieves stable incremental memory with limited storage requirements.
• Score: 32.18013657468068
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The ability of humans to rapidly learn new knowledge while retaining old memories poses a significant challenge for current deep learning models. To handle this challenge, we draw inspiration from human memory and learning mechanisms and propose the Self-Reflective Complementary Incremental System (SR-CIS). Comprising the deconstructed Complementary Inference Module (CIM) and Complementary Memory Module (CMM), SR-CIS features a small model for fast inference and a large model for slow deliberation in CIM, enabled by the Confidence-Aware Online Anomaly Detection (CA-OAD) mechanism for efficient collaboration. CMM consists of task-specific Short-Term Memory (STM) region and a universal Long-Term Memory (LTM) region. By setting task-specific Low-Rank Adaptive (LoRA) and corresponding prototype weights and biases, it instantiates external storage for parameter and representation memory, thus deconstructing the memory module from the inference module. By storing textual descriptions of images during training and combining them with the Scenario Replay Module (SRM) post-training for memory combination, along with periodic short-to-long-term memory restructuring, SR-CIS achieves stable incremental memory with limited storage requirements. Balancing model plasticity and memory stability under constraints of limited storage and low data resources, SR-CIS surpasses existing competitive baselines on multiple standard and few-shot incremental learning benchmarks.

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