Autoencoder-Based Hybrid Replay for Class-Incremental Learning

Milad Khademi Nori, IL MIN KIM, Guanghui Wang

Keywords: Catastrophic Forgetting, Class-Incremental Learning, Continual Learning, Task Confusion.

TL;DR: A memory-efficient architecture for incremental learning based on model-based and exemplar-based approach.

Abstract: In class-incremental learning (CIL), effective incremental learning strategies are essential to mitigate task confusion and catastrophic forgetting, especially as the number of tasks $t$ increases. Current exemplar replay strategies impose $\mathcal{O}(t)$ memory/compute complexities. We propose an autoencoder-based hybrid replay (AHR) strategy that leverages our new hybrid autoencoder (HAE) to function as a compressor to alleviate the requirement for large memory, achieving $\mathcal{O}(0.1 t)$ at the worst case with the computing complexity of $\mathcal{O}(t)$ while accomplishing state-of-the-art performance. The decoder later recovers the exemplar data stored in the latent space, rather than in raw format. Additionally, HAE is designed for both discriminative and generative modeling, enabling classification and replay capabilities, respectively. HAE adopts the charged particle system energy minimization equations and repulsive force algorithm for the incremental embedding and distribution of new class centroids in its latent space. Our results demonstrate that AHR consistently outperforms recent baselines across multiple benchmarks while operating with the same memory/compute budgets.

Primary Area: transfer learning, meta learning, and lifelong learning

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Submission Number: 2656