The Forward-Forward algorithm has evolved in machine learning research, tackling more complex tasks that mimic real-life applications. In the last years, it has been improved by several techniques to perform better than its original version, handling a challenging dataset like CIFAR10 without losing its flexibility and low memory usage. We have shown in our results that improvements are achieved through a combination of convolutional channel grouping, learning rate schedules, and independent block structures during training that lead to a 20\% decrease in test error percentage. Additionally, to approach further implementations on low-capacity hardware projects we have presented a series of lighter models that achieve low test error percentages within (216)\% and number of trainable parameters between 164,706 and 754,386. This serving also as a basis for our future study on complete verification and validation of these kinds of neural networks.
View on arXiv@article{torres2025_2504.21662,
title={ On Advancements of the Forward-Forward Algorithm },
author={ Mauricio Ortiz Torres and Markus Lange and Arne P. Raulf },
journal={arXiv preprint arXiv:2504.21662},
year={ 2025 }
}