Abstract
The Forward-Forward algorithm (FF) is yet another novel invention by Geoffrey Hinton, the creator of the famous backpropagation algorithm (BP). Since its proposal, many papers have been published exploring its potential, and good progress has been made in increasing its viability. Though FF continually falls short of BP, its purpose is not to replace BP and preliminary research shows that there is plenty of room for growth. In this paper, we present a literature review for FF algorithms applied to Convolution Neural Networks (CNN) for image classification tasks and set the stage for applying FF to more complex datasets. The proposed Ripple algorithm creates spatially distributed class representations that maintain label visibility throughout convolutional layers, addressing a key limitation in applying FF to CNN architectures. Our Ripple algorithm plays a key role in expanding the embedding space which provides the goodness function greater flexibility to fit the data. We evaluate our approach using the MNIST and CIFAR-10 datasets. Our experimental results demonstrate that the Ripples technique enables FF-trained CNNs to achieve competitive performance while maintaining the theoretical advantages of forward-only learning.
Advisor
Naseef Mansoor
Committee Member
Rushit Dave
Committee Member
Rajeev Bukralia
Date of Degree
2025
Language
english
Document Type
Thesis
Degree
Master of Science (MS)
Program of Study
Data Science
Department
Computer Information Science
College
Science, Engineering and Technology
Recommended Citation
Connolly, S. (2025). RIPPLES: An automated embedding generation algorithm for the Forward-Forward Algorithm [Master’s thesis, Minnesota State University, Mankato]. Cornerstone: A Collection of Scholarly and Creative Works for Minnesota State University, Mankato. https://cornerstone.lib.mnsu.edu/etds/1569/
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
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