DTG-FF reaches 91.8% on CIFAR-10 and 49.4% on ImageNet-100 224x224 but BP baselines beat it by 2.4-5.93 pp with gaps widening by class count on real data while reversing the synthetic trend.
Local signal adaptation in the forward-forward algorithm.arXiv preprint arXiv:2305.12466,
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Synthetic Benchmarks Overstate Forward-Forward Scaling: Real-Data Limits of Layer-Local Training
DTG-FF reaches 91.8% on CIFAR-10 and 49.4% on ImageNet-100 224x224 but BP baselines beat it by 2.4-5.93 pp with gaps widening by class count on real data while reversing the synthetic trend.