aiqbench analysis
This paper introduces a learnable channel-class assignment mechanism for Forward-Forward algorithms, enabling adaptive specialization of convolutional channels that significantly narrows the performance gap with backpropagation. By integrating loss-aware layer weighting and regularization techniques, the approach achieves state-of-the-art results on standard benchmarks like CIFAR-10 and Tiny-ImageNet while maintaining a biologically inspired, forward-only inference paradigm. The work demonstrates that dynamic channel specialization substantially enhances the representational capacity of non-gradient-based deep learning models.
Forward-Only Convolutional Neural Networks with Learnable Channel-Class Assignment
arXiv:2606.09928v1 Announce Type: new Abstract: The Forward-Forward (FF) algorithm offers a biologically inspired alternative to backpropagation by replacing gradient-based credit assignment with local, forward-only objectives. While recent extensions have adapted FF to convolutional neural networks (CNNs), existing formulations rely on static channel-class partitions and struggle to perform effectively in complex tasks. In this work, we introduce a learnable channel-class assignment mechanism that enables adaptive, data-driven specialization of convolutional channels, supported by entropy and orthogonality regularization to promote learning performance. We further propose a loss-aware layer contribution strategy that adaptively weights intermediate-layer predictions based on their validation performance, enhancing the effectiveness of forward-only inference. Integrated into residual CNNs, the proposed method achieves consistently superior performance across CIFAR-10, CIFAR-100, and Tiny-ImageNet compared to existing similar forward-only methods. Notably, it establishes new state-of-the-art performance among FF-based models, substantially narrowing the gap with backpropagation. These findings demonstrate that introducing learnable channel specialization and layer contribution weighting significantly enhances the representational capacity of forward-only learning in deep CNNs.
Abstract:The Forward-Forward (FF) algorithm offers a biologically inspired alternative to backpropagation by replacing gradient-based credit assignment with local, forward-only objectives. While recent extensions have adapted FF to convolutional neural networks (CNNs), existing formulations rely on static channel-class partitions and struggle to perform effectively in complex tasks. In this work, we introduce a learnable channel-class assignment mechanism that enables adaptive, data-driven specialization of convolutional channels, supported by entropy and orthogonality regularization to promote learning performance. We further propose a loss-aware layer contribution strategy that adaptively weights intermediate-layer predictions based on their validation performance, enhancing the effectiveness of forward-only inference. Integrated into residual CNNs, the proposed method achieves consistently superior performance across CIFAR-10, CIFAR-100, and Tiny-ImageNet compared to existing similar forward-only methods. Notably, it establishes new state-of-the-art performance among FF-based models, substantially narrowing the gap with backpropagation. These findings demonstrate that introducing learnable channel specialization and layer contribution weighting significantly enhances the representational capacity of forward-only learning in deep CNNs.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2606.09928 [cs.LG]
(or arXiv:2606.09928v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.09928 Focus to learn more arXiv-issued DOI via DataCite
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