Class Incremental Continual Learning with Self-Organizing Maps and Variational Autoencoders Using Synthetic Replay

• URL: http://arxiv.org/abs/2508.21240v1
• Date: Thu, 28 Aug 2025 22:11:22 GMT
• Title: Class Incremental Continual Learning with Self-Organizing Maps and Variational Autoencoders Using Synthetic Replay
• Authors: Pujan Thapa, Alexander Ororbia, Travis Desell,
• Abstract summary: This work introduces a novel generative continual learning framework based on self-organizing maps (SOMs) and variational autoencoders (VAEs)<n>For high-dimensional input spaces, such as of CIFAR-10 and CIFAR-100, we design a scheme where the SOM operates over the latent space learned by a VAE.<n>For lower-dimensional inputs, such as those found in MNIST and FashionMNIST, the SOM operates in a standalone fashion.
• Score: 47.020758735910306
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This work introduces a novel generative continual learning framework based on self-organizing maps (SOMs) and variational autoencoders (VAEs) to enable memory-efficient replay, eliminating the need to store raw data samples or task labels. For high-dimensional input spaces, such as of CIFAR-10 and CIFAR-100, we design a scheme where the SOM operates over the latent space learned by a VAE, whereas, for lower-dimensional inputs, such as those found in MNIST and FashionMNIST, the SOM operates in a standalone fashion. Our method stores a running mean, variance, and covariance for each SOM unit, from which synthetic samples are then generated during future learning iterations. For the VAE-based method, generated samples are then fed through the decoder to then be used in subsequent replay. Experimental results on standard class-incremental benchmarks show that our approach performs competitively with state-of-the-art memory-based methods and outperforms memory-free methods, notably improving over best state-of-the-art single class incremental performance on CIFAR-10 and CIFAR-100 by nearly $10$\% and $7$\%, respectively. Our methodology further facilitates easy visualization of the learning process and can also be utilized as a generative model post-training. Results show our method's capability as a scalable, task-label-free, and memory-efficient solution for continual learning.

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