Adaptive Multi-Scale Goodness Aggregation for Forward-Forward Learning

This paper introduces Adaptive Multi-Scale Goodness Aggregation (AMSGA), a novel extension of the Forward-Forward (FF) algorithm to enhance stability, robustness, and generalization in local-learning neural networks. AMSGA incorporates multi-scale goodness aggregation, adaptive hard negative mining, and layer-dependent thresholds. Experiments on MNIST and Fashion-MNIST show consistent performance improvements over the baseline FF algorithm.