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Learning discriminative features within forward-Forward algorithm using convolutional prototype
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DOI：10.1016/j.patcog.2026.113139.png)
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Abstract
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• We introduce prototype learning in forward-forward (FF) algorithm for the first time. • We propose PLFF to improve properties of features learned by FF algorithm. • We evaluate FF methods on long-tailed datasets for the first time. • Our PLFF achieves state-of-the-art performance across various public datasets.
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Cited Papers
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