🤖 AI Summary
This work proposes an enhanced local learning framework to address the limitations of the original Forward-Forward algorithm in stability, robustness, and generalization. By integrating multi-scale goodness aggregation, layer-wise adaptive thresholds, adaptive curriculum-guided hard negative mining, and a warm-up cosine annealing learning rate schedule, the training dynamics are effectively optimized. The proposed method significantly improves performance while preserving biological plausibility and memory efficiency, achieving accuracy gains of up to 1.45% on MNIST and 1.50% on Fashion-MNIST without introducing noticeable computational overhead.
📝 Abstract
We propose Adaptive Multi-Scale Goodness Aggregation (AMSGA), a novel extension of the Forward-Forward (FF) algorithm designed to improve stability, robustness, and generalization in local-learning neural networks. AMSGA addresses several limitations of the original FF framework by introducing multi-scale goodness aggregation across local, intermediate, and global representations; adaptive curriculum-guided hard negative mining; layer-dependent adaptive thresholds; and a warm-up cosine annealing learning-rate schedule for improved optimization stability. Together, these modifications strengthen the FF paradigm while preserving its biologically plausible and memory-efficient properties. Experiments on MNIST and Fashion-MNIST demonstrate consistent performance improvements over the baseline FF algorithm, achieving up to +1.45% improvement on MNIST and +1.50% improvement on Fashion-MNIST without significant computational overhead. Our results suggest that local learning methods can become substantially more competitive when goodness estimation and training dynamics are carefully designed.