Learnable channel-class assignment and adaptive layer weighting allow forward-only CNNs to reach new state-of-the-art results among FF models on CIFAR-10, CIFAR-100, and Tiny-ImageNet.
J., and Oramas, J
3 Pith papers cite this work. Polarity classification is still indexing.
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2026 3verdicts
UNVERDICTED 3representative citing papers
HCL-FF augments the Forward-Forward algorithm with hierarchical learning and contrastive objectives to reach new state-of-the-art accuracies among FF methods on CIFAR-10 (+5.46%), CIFAR-100 (+17.00%), and Tiny-ImageNet (+12.51%).
Cumulative-goodness Forward-Forward networks exhibit layer free-riding where discrimination gradients decay exponentially with prior positive margins; per-block, hardness-gated, and depth-scaled remedies yield 4-45x better layer separation but <1% accuracy change on CIFAR and Tiny ImageNet.
citing papers explorer
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Forward-Only Convolutional Neural Networks with Learnable Channel-Class Assignment
Learnable channel-class assignment and adaptive layer weighting allow forward-only CNNs to reach new state-of-the-art results among FF models on CIFAR-10, CIFAR-100, and Tiny-ImageNet.
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HCL-FF: Hierarchical and Contrastive Learning for Forward-Forward Algorithm
HCL-FF augments the Forward-Forward algorithm with hierarchical learning and contrastive objectives to reach new state-of-the-art accuracies among FF methods on CIFAR-10 (+5.46%), CIFAR-100 (+17.00%), and Tiny-ImageNet (+12.51%).
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Cumulative-Goodness Free-Riding in Forward-Forward Networks: Real, Repairable, but Not Accuracy-Dominant
Cumulative-goodness Forward-Forward networks exhibit layer free-riding where discrimination gradients decay exponentially with prior positive margins; per-block, hardness-gated, and depth-scaled remedies yield 4-45x better layer separation but <1% accuracy change on CIFAR and Tiny ImageNet.