C3GAN: A brain-inspired memory consolidation for class-incremental learning

Human cognition excels in lifelong learning, seamlessly acquiring, retaining, and transferring knowledge. In contrast, deep neural networks suffer from catastrophic forgetting, where training on new tasks rapidly erases previously acquired knowledge. In the human brain, memory reactivation is crucial for preserving memories over time. Similarly, generative replay in artificial neural networks shows potential for addressing forgetting by reactivating learned representations. However, existing generative replay techniques struggle with complex tasks and high-dimensional data, as they shift the burden from the task-solving network to the generative network, which is also prone to catastrophic forgetting. In this paper, we propose C3GAN, a brain-inspired model that combines Contrastive Clustering and Conditional Generative Adversarial Networks to emulate the memory consolidation processes of the brain. C3GAN uses contrastive class structuring to consolidate recent memories, mimicking hippocampal functions, and incorporates a conditional generative adversarial network to facilitate long-term knowledge storage, akin to the prefrontal cortex. Additionally, an amygdala-inspired module enhances selective replay of indistinguishable classes by prioritizing memory that is emotionally salient, similar to the amygdala's role in strengthening the retention of significant experiences. C3GAN achieves state-of-the-art performance on class-incremental learning benchmarks without raw data, providing a novel solution for lifelong memory retention in artificial systems.