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Updated: Sep 17, 2025

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Self-Contrastive Forward-Forward algorithm.

Xing Chen1, Dongshu Liu2, Jérémie Laydevant3,4

  • 1Laboratoire Albert Fert, CNRS, Thales, Université Paris-Saclay, Palaiseau, France. xing.chen@cnrs-thales.fr.

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Summary
This summary is machine-generated.

The Self-Contrastive Forward-Forward (SCFF) algorithm improves autonomous system training by enhancing unsupervised learning. SCFF achieves competitive performance on benchmark datasets, enabling real-time learning on edge devices.

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Deep Learning

Background:

  • Autonomous agents require lifelong learning capabilities compatible with decentralized systems and limited resources.
  • Traditional backpropagation for training neural networks requires transpose operations, which are incompatible with purely feedforward approaches.
  • The Forward-Forward (FF) algorithm offers a purely feedforward training method but struggles with performance due to unreliable negative data generation in unsupervised learning.

Purpose of the Study:

  • To introduce the Self-Contrastive Forward-Forward (SCFF) algorithm, a novel training method designed to enhance the performance of the FF algorithm.
  • To address the limitations of existing unsupervised learning methods within the FF framework by improving negative data generation.
  • To expand the applicability of FF-based algorithms to sequential data tasks by integrating them with recurrent neural networks.

Main Methods:

  • The proposed Self-Contrastive Forward-Forward (SCFF) algorithm is inspired by self-supervised contrastive learning techniques.
  • SCFF generates both positive and negative data samples suitable for diverse datasets, overcoming limitations of previous FF negative data generation.
  • The study extends the FF algorithm's application to recurrent neural networks for processing sequential data.

Main Results:

  • SCFF demonstrates superior performance compared to existing unsupervised local learning algorithms on multiple benchmark datasets (MNIST, CIFAR-10, STL-10, Tiny ImageNet).
  • The algorithm successfully extends the FF approach to training recurrent neural networks, proving effective for sequential data.
  • The research validates SCFF as a competitive training method for unsupervised local learning.

Conclusions:

  • The SCFF algorithm significantly closes the performance gap for the Forward-Forward algorithm in unsupervised learning tasks.
  • This work enables high-accuracy, real-time learning for resource-constrained edge devices by improving autonomous system training.
  • The successful application to recurrent neural networks broadens the utility of FF-based methods for diverse machine learning applications.