LightCL
LightCL is a compact continual learning algorithm designed for edge devices with limited memory. It addresses the challenge of learning new tasks without forgetting previous ones by analyzing the generalizability of neural network layers, which varies significantly across different layers. LightCL maintains generalizability by freezing well-generalized parts of the network, avoiding costly retraining, and memorizes feature patterns by stabilizing feature extraction of prior tasks for less-generalized parts using a small memory buffer. This approach reduces memory footprint by up to 6.16x compared to state-of-the-art continual learning methods while maintaining or improving performance. The algorithm supports CIFAR10 and TinyImageNet datasets, works with PyTorch, and offers both standard and sparse training modes. Configurable parameters include learning rate, regulation loss weight, memory buffer size, vital feature map ratio, random seed, pretrained model usage, dataset selection, and sparsity. A pretrained