Adaptive Multi-Scale Goodness Aggregation for Forward-Forward Learning
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Computer Science > Machine Learning
Title:Adaptive Multi-Scale Goodness Aggregation for Forward-Forward Learning
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.
| Comments: | 6 pages, 5 tables, IEEE format |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2605.18804 [cs.LG] |
| (or arXiv:2605.18804v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.18804
arXiv-issued DOI via DataCite (pending registration)
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