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. Our source code and supplementary material are available at https://github.com/andreaspapac/CwComp.

This paper introduces a Channel-wise Goodness Function (CWG) that enhances the Forward-Forward through the use of Convolutional Neural Networks.The CWG function facilitates simultaneous feature extraction and separation, eliminating the requirement for constructing negative data and leading to faster convergence rates.The approach employs a two-component loss function that maximizes positive goodness and minimizes negative goodness.This enables the model to learn class-specific features to outperform recent non-backpropagation approaches on basic image classification datasets and shorten the gap with the well-established backpropagation methods.