提出新型前向-前向算法，实现单次前传完成训练与推理。
Hyperspherical Forward-Forward with Prototypical Representations
- 在超球面空间中用类原型作为几何锚点，直接进行多分类。
- 推理速度比原方法快40倍以上，ImageNet上达65.96%迁移精度。
- 适合追求高效本地学习的视觉模型研究者。
前向-前向（FF）算法为反向传播提供了生物启发的替代方案，但其推理过程需对每个类别单独前传，计算成本极高。本文提出超球面前向-前向（HFF），将每层的局部目标从二元拟合重构为超球面特征空间中的直接多分类任务。通过学习一组单位范数的类特定原型作为几何锚点与隐式负样本，该方法在保持局部训练优势的同时，实现权重更新与推理仅需一次前向传播，使效率提升超过40倍。方法简单易实现，可扩展至现代卷积架构，在标准图像分类基准上表现优异，逼近反向传播性能。尤其重要的是，我们是首批在ImageNet-1k上实现超过25%准确率的贪婪局部学习方法，迁移学习下达到65.96%。
原文摘要 · Abstract (English)
The Forward-Forward (FF) algorithm presents a compelling, bio-inspired alternative to backpropagation. However, while efficient in training, it has a computationally prohibitive inference process that requires a separate forward pass for every class that is evaluated. In this work, we introduce the Hyperspherical Forward-Forward (HFF), a novel reformulation that resolves this critical bottleneck. Our core innovation is to reframe the local objective of each layer from a binary goodness-of-fit task to a direct multi-class classification problem within a hyperspherical feature space. We achieve this by learning a set of class-specific, unit-norm prototypes that act as geometric anchors and implicit negatives. This architectural innovation preserves the benefits of local training while enabling weight update and inference in a single forward pass, making it >40x faster than the original FF algorithm. Our method is simple to implement, scales effectively to modern convolutional architectures, and achieves superior accuracy on standard image classification benchmarks, closing the gap with backpropagation. Most notably, we are among the first greedy local-learning methods to report over 25% top-1 accuracy on ImageNet-1k, and 65.96% with transfer learning.
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