HFF replaces binary goodness-of-fit in Forward-Forward with hyperspherical prototypes for direct multi-class decisions, enabling single-forward-pass inference and training that scales to ImageNet while closing much of the gap to backpropagation.
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Hyperspherical Forward-Forward with Prototypical Representations
HFF replaces binary goodness-of-fit in Forward-Forward with hyperspherical prototypes for direct multi-class decisions, enabling single-forward-pass inference and training that scales to ImageNet while closing much of the gap to backpropagation.