NICE: Neurogenesis Inspired Contextual Encoding for Replay-free Class Incremental Learning
Mustafa Burak Gürbüz, Jean Michael Moorman, Constantine Dovrolis · 2024
Deep neural networks (DNNs) struggle to learn in dy-namic settings because they mainly rely on static datasets. Continual learning (CL) aims to overcome this limitation by enabling DNNs to incrementally accumulate knowledge. A widely adopted scenario in CL is class-incremental learning (CIL), where DNNs are required to sequentially learn more classes. Among the various strategies in CL, replay methods, which revisit previous classes, stand out as the only effective ones in CIL. Other strategies, such as ar-chitectural modifications to segregate information across weights and protect them from change, are ineffective in CIL. This is because they need additional information during testing to select the correct network parts to use. In this paper, we propose NICE, Neurogenesis Inspired Contex-tual Encoding, a replay-free architectural method inspired by adult neurogenesis in the hippocampus. NICE groups neurons in the DNN based on different maturation stages and infers which neurons to use during testing without any additional signal. Through extensive experiments across 6 datasets and 3 architectures, we show that NICE performs on par with or often outperforms replay methods. We also make the case that neurons exhibit highly distinctive acti-vation patterns for the classes in which they specialize, en-abling us to determine when they should be used. The code is available at https://github.com/BurakGurbuz97/NICE.