Reversible Neural Networks for Continual Learning with No Memory Footprint

Abstract

Deep learning presents a significant challenge for constant learning defined as a model's ability to acquire & adapt to latest tasks while keeping previously obtained knowledge because of catastrophic forgetting, wherein the latest learning interacts with existing knowledge. This study offers a memory-efficient alternative that eliminates the necessity for storing previous information or model snapshots by use of Reversible Neural Networks (RevNets), therefore addressing this problem. Unlike conventional methods depending on external memory buffers or complex regularization methods, RevNets enable the complete reconstruction of intermediate activations during backpropagation, enabling the network to "retain" past computations without incurring additional memory costs. Our approach shows a great resistance to forgetting while keeping scalability and efficiency by using this unique feature to sustain task performance throughout sequential learning assignments. We provide a complete strategy integrating reversibility into conventional neural networks and assess our approach across many continuous learning benchmarks including visual categorization and sequential task learning. The results show that our approach not only achieves comparable performance in relation to leading memory-intensive methods but also greatly lowers the computational load. This work presents a fresh perspective on continuous learning by proving that architectural design especially reversibility can greatly lower forgetting even without outside memory resources. Where memory & computational efficiency are too critical, the proposed method offers great possibilities for on-device learning, edge computing & eternal AI systems