# Semi-parametric Memory Consolidation: Towards Brain-like Deep Continual Learning Source: [Semi-parametric Memory Consolidation: Towards Brain-like Deep Continual Learning]() Collection index: [llms.txt]() Data updated: 2025-10-26T04:32:25.977Z Authors: Geng Liu, Fei Zhu, Rong Feng, Zhiqiang Yi, Shiqi Wang, Gaofeng Meng, Zhaoxiang Zhang Published: 2025-04-20T19:53:13.000Z ## Abstract Humans and most animals inherently possess a distinctive capacity to continually acquire novel experiences and accumulate worldly knowledge over time. This ability, termed continual learning, is also critical for deep neural networks (DNNs) to adapt to the dynamically evolving world in open environments. However, DNNs notoriously suffer from catastrophic forgetting of previously learned knowledge when trained on sequential tasks. In this work, inspired by the interactive human memory and learning system, we propose a novel biomimetic continual learning framework that integrates semi-parametric memory and the wake-sleep consolidation mechanism. For the first time, our method enables deep neural networks to retain high performance on novel tasks while maintaining prior knowledge in real-world challenging continual learning scenarios, e.g., class-incremental learning on ImageNet. This study demonstrates that emulating biological intelligence provides a promising path to enable deep neural networks with continual learning capabilities. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()