Learning discriminative features within forward-Forward algorithm using convolutional prototype

Compared to back-propagation algorithms, the forward-forward (FF) algorithm proposed by Hinton [1] can in parallel optimize all layers of deep network models, while requiring less storage and achieving higher computational efficiency. However, the current FF methods cannot fully leverage the label information of samples, which suppress the learning of discriminative features. In this paper, we propose prototype learning within the FF algorithm (PLFF). When optimizing each convolutional layer, PLFF first divides the convolutional kernels into various groups according to the number K of classes, which serve as class prototypes in the optimizing, referred to as convolutional prototypes. For every sample, K goodness scores are calculated based on its convolutional results between the sample data and the convolutional prototypes. Then, using multiple binary cross-entropy losses, PLFF maximizes the positive goodness score corresponding to the sample label while minimizing other negative goodness scores, to learn discriminative features. Meanwhile, PLFF maximizes the cosine distances among the K convolutional prototypes, which enhances their discrimination and, in turn, promotes the learning of features. The image classification results across multiple datasets show that PLFF achieves the best results among different FF methods. Finally, for the first time, we verify the long-tailed recognition performance of different FF methods, demonstrating that our PLFF achieves superior results.