Convolutional Channel-wise Competitive Learning for the Forward-Forward Algorithm

• URL: http://arxiv.org/abs/2312.12668v1
• Date: Tue, 19 Dec 2023 23:48:43 GMT
• Title: Convolutional Channel-wise Competitive Learning for the Forward-Forward Algorithm
• Authors: Andreas Papachristodoulou, Christos Kyrkou, Stelios Timotheou, Theocharis Theocharides
• Abstract summary: Forward-Forward (FF) algorithm has been proposed to alleviate the issues of backpropagation (BP) commonly used to train deep neural networks. We take the main ideas of FF and improve them by leveraging channel-wise competitive learning in the context of convolutional neural networks for image classification tasks. Our method outperforms recent FF-based models on image classification tasks, achieving testing errors of 0.58%, 7.69%, 21.89%, and 48.77% on MNIST, Fashion-MNIST, CIFAR-10 and CIFAR-100 respectively.
• Score: 5.1246638322893245
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The Forward-Forward (FF) Algorithm has been recently proposed to alleviate the issues of backpropagation (BP) commonly used to train deep neural networks. However, its current formulation exhibits limitations such as the generation of negative data, slower convergence, and inadequate performance on complex tasks. In this paper, we take the main ideas of FF and improve them by leveraging channel-wise competitive learning in the context of convolutional neural networks for image classification tasks. A layer-wise loss function is introduced that promotes competitive learning and eliminates the need for negative data construction. To enhance both the learning of compositional features and feature space partitioning, a channel-wise feature separator and extractor block is proposed that complements the competitive learning process. Our method outperforms recent FF-based models on image classification tasks, achieving testing errors of 0.58%, 7.69%, 21.89%, and 48.77% on MNIST, Fashion-MNIST, CIFAR-10 and CIFAR-100 respectively. Our approach bridges the performance gap between FF learning and BP methods, indicating the potential of our proposed approach to learn useful representations in a layer-wise modular fashion, enabling more efficient and flexible learning.

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