DMF-Archive/PILF: A Cutting-Edge Continual Learning Framework Inspired by IPWT to Combat Catastrophic Forgetting and Enhance Efficiency through a Surprise-Gated Mixture of Experts Model.

The document introduces a cognitive learning framework, PILF (Predictive Integrity-driven Learning Framework), aiming to enhance training processes in neural networks. Unlike traditional methods that use fixed hyperparameters like learning rates, PILF adapts these parameters dynamically based on the “surprise” value from data batches. This real-time adjustment allows models to optimize their learning intensity and the number of activated experts depending on the complexity of tasks. Key features include the PILR-S module for optimizing learning rates and the full PILF framework, which integrates both learning rate and capacity adjustments in a Mixture-of-Experts architecture. The approach minimizes resource usage for simpler tasks while enabling more robust learning for complex datasets. Experimental setups utilizing lightweight Vision Transformer architectures assess different learning strategies, showcasing the efficiency and effectiveness of the PILF methodology. The project emphasizes understanding model behavior as integral to training, fostering deeper cognitive learning principles.

Source link