Combining convolutional channel grouping, channel-wise loss, chunked local updates, and last-layer inference reduces CIFAR10 test error for Forward-Forward networks, with lightweight models reaching about 19-24% error.
Convolutional channel-wise competitive learning for the forward-forward algorithm
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On Advancements of the Forward-Forward Algorithm
Combining convolutional channel grouping, channel-wise loss, chunked local updates, and last-layer inference reduces CIFAR10 test error for Forward-Forward networks, with lightweight models reaching about 19-24% error.