# Triple Memory Networks: a Brain-Inspired Method for Continual Learning

arXiv: https://arxiv.org/abs/2003.03143 · HTML: https://mlsanity.com/paper/2003.03143/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Mar 2020.

## Built on by (most cited first, 28 of 28)

- [A Comprehensive Survey of Continual Learning: Theory, Method and Application](https://mlsanity.com/paper/2302.00487/) (2023)
- [Class-Incremental Learning: A Survey](https://mlsanity.com/paper/2302.03648/) (2023)
- [SLCA: Slow Learner with Classifier Alignment for Continual Learning on a Pre-trained Model](https://mlsanity.com/paper/2303.05118/) (2023)
- [Always Be Dreaming: A New Approach for Data-Free Class-Incremental Learning](https://mlsanity.com/paper/2106.09701/) (2021)
- [AFEC: Active Forgetting of Negative Transfer in Continual Learning](https://mlsanity.com/paper/2110.12187/) (2021)
- [Don't Stop Learning: Towards Continual Learning for the CLIP Model](https://mlsanity.com/paper/2207.09248/) (2022)
- [Incorporating Neuro-Inspired Adaptability for Continual Learning in Artificial Intelligence](https://mlsanity.com/paper/2308.14991/) (2023)
- [A Survey on Continual Semantic Segmentation: Theory, Challenge, Method and Application](https://mlsanity.com/paper/2310.14277/) (2023)
- [HiDe-PET: Continual Learning via Hierarchical Decomposition of Parameter-Efficient Tuning](https://mlsanity.com/paper/2407.05229/) (2024)
- [A Comprehensive Survey of Convolutions in Deep Learning: Applications, Challenges, and Future Trends](https://mlsanity.com/paper/2402.15490/) (2024)
- [SLCA++: Unleash the Power of Sequential Fine-tuning for Continual Learning with Pre-training](https://mlsanity.com/paper/2408.08295/) (2024)
- [ATLAS: Adapter-Based Multi-Modal Continual Learning with a Two-Stage Learning Strategy](https://mlsanity.com/paper/2410.10923/) (2024)
- [Towards Incremental Learning in Large Language Models: A Critical Review](https://mlsanity.com/paper/2404.18311/) (2024)
- [Joint Diffusion models in Continual Learning](https://mlsanity.com/paper/2411.08224/) (2024)
- [Decomposing the Basic Abilities of Large Language Models: Mitigating Cross-Task Interference in Multi-Task Instruct-Tuning](https://mlsanity.com/paper/2605.05676/) (2026)
- [IF2Net: Innately Forgetting-Free Networks for Continual Learning](https://mlsanity.com/paper/2306.10480/) (2023)
- [C-LoRA: Continual Low-Rank Adaptation for Pre-trained Visual Models](https://mlsanity.com/paper/2502.17920/) (2025)
- [Hessian Aware Low-Rank Perturbation for Order-Robust Continual Learning](https://mlsanity.com/paper/2311.15161/) (2023)
- [Contrastive Federated Learning with Tabular Data Silos](https://mlsanity.com/paper/2409.06123/) (2024)
- [HiCL: Hippocampal-Inspired Continual Learning](https://mlsanity.com/paper/2508.16651/) (2025)
- [ESSENTIAL: Episodic and Semantic Memory Integration for Video Class-Incremental Learning](https://mlsanity.com/paper/2508.10896/) (2025)
- [Provable Effects of Data Replay in Continual Learning: A Feature Learning Perspective](https://mlsanity.com/paper/2602.02767/) (2026)
- [E2-AEN: End-to-End Incremental Learning with Adaptively Expandable Network](https://mlsanity.com/paper/2207.06754/) (2022)
- [Complementary Learning Subnetworks for Parameter-Efficient Class-Incremental Learning](https://mlsanity.com/paper/2306.11967/) (2023)
- [Brain-Inspired Continual Learning-Robust Feature Distillation and Re-Consolidation for Class Incremental Learning](https://mlsanity.com/paper/2404.14588/) (2024)
- [Freeze and Cluster: A Simple Baseline for Rehearsal-Free Continual Category Discovery](https://mlsanity.com/paper/2503.09106/) (2025)
- [Enhancing Pre-Trained Model-Based Class-Incremental Learning through Neural Collapse](https://mlsanity.com/paper/2504.18437/) (2025)
- [Hierarchical Dual-Subspace Decoupling for Continual Learning in Vision-Language Models](https://mlsanity.com/paper/2605.07512/) (2026)

## Abstract (arXiv)

Continual acquisition of novel experience without interfering previously learned knowledge, i.e. continual learning, is critical for artificial neural networks, but limited by catastrophic forgetting. A neural network adjusts its parameters when learning a new task, but then fails to conduct the old tasks well. By contrast, the brain has a powerful ability to continually learn new experience without catastrophic interference. The underlying neural mechanisms possibly attribute to the interplay of hippocampus-dependent memory system and neocortex-dependent memory system, mediated by prefrontal cortex. Specifically, the two memory systems develop specialized mechanisms to consolidate information as more specific forms and more generalized forms, respectively, and complement the two forms of information in the interplay. Inspired by such brain strategy, we propose a novel approach named triple memory networks (TMNs) for continual learning. TMNs model the interplay of hippocampus, prefrontal cortex and sensory cortex (a neocortex region) as a triple-network architecture of generative adversarial networks (GAN). The input information is encoded as specific representation of the data distributions in a generator, or generalized knowledge of solving tasks in a discriminator and a classifier, with implementing appropriate brain-inspired algorithms to alleviate catastrophic forgetting in each module. Particularly, the generator replays generated data of the learned tasks to the discriminator and the classifier, both of which are implemented with a weight consolidation regularizer to complement the lost information in generation process. TMNs achieve new state-of-the-art performance on a variety of class-incremental learning benchmarks on MNIST, SVHN, CIFAR-10 and ImageNet-50, comparing with strong baseline methods.