Adaptive Spatial Goodness Encoding: Advancing and Scaling Forward-Forward Learning Without Backpropagation

• URL: http://arxiv.org/abs/2509.12394v1
• Date: Mon, 15 Sep 2025 19:38:32 GMT
• Title: Adaptive Spatial Goodness Encoding: Advancing and Scaling Forward-Forward Learning Without Backpropagation
• Authors: Qingchun Gong, Robert Bogdan Staszewski, Kai Xu,
• Abstract summary: We propose a new Forward-Forward (FF)-based training framework tailored for convolutional neural networks (CNNs)<n>ASGE features maps to compute spatially-aware goodness rep- resentations at each layer, enabling layer-wise supervision.<n>We present the first successful ap- plication of FF-based training to ImageNet datasets, with Top-1 and Top-5 accuracies of 26.21% and 47.49%.
• Score: 5.092009068303438
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The Forward-Forward (FF) algorithm offers a promising al- ternative to backpropagation (BP). Despite advancements in recent FF-based extensions, which have enhanced the origi- nal algorithm and adapted it to convolutional neural networks (CNNs), they often suffer from limited representational ca- pacity and poor scalability to large-scale datasets, primarily due to exploding channel dimensionality. In this work, we propose adaptive spatial goodness encoding (ASGE), a new FF-based training framework tailored for CNNs. ASGE lever- ages feature maps to compute spatially-aware goodness rep- resentations at each layer, enabling layer-wise supervision. Crucially, this approach decouples classification complexity from channel dimensionality, thereby addressing the issue of channel explosion and achieving competitive performance compared to other BP-free methods. ASGE outperforms all other FF-based approaches across multiple benchmarks, delivering test accuracies of 99.65% on MNIST, 93.41% on FashionMNIST, 90.62% on CIFAR-10, and 65.42% on CIFAR-100. Moreover, we present the first successful ap- plication of FF-based training to ImageNet, with Top-1 and Top-5 accuracies of 26.21% and 47.49%. By entirely elimi- nating BP and significantly narrowing the performance gap with BP-trained models, the ASGE framework establishes a viable foundation toward scalable BP-free CNN training.

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