forward-forward-learning

Forward-forward learning uses two forward passes—one on positive data and one on negative data—to compute layer-wise goodness scores. Each layer maximizes the difference between positive and negative goodness locally, eliminating the need for backpropagation while enabling per-layer optimization.

Yes. Forward-forward is designed for memory-efficient on-chip training and neuromorphic hardware. It eliminates gradient storage overhead and enables parallel layer-wise updates, making it suitable for edge devices and resource-constrained environments.

The goodness function G(h) = sum h_i² measures the total squared activation magnitude per layer. Forward-forward learns by maximizing G(h⁺) − G(h⁻), where h⁺ and h⁻ are activations from positive and negative data passes respectively.

Forward-forward uses local layer-wise objectives and self-contrastive positive/negative passes instead of global gradient computation. This enables biologically plausible learning, parallel updates across layers, and reduced memory footprint compared to backpropagation.

Yes. Forward-forward supports self-contrastive extensions where negative samples are self-generated, enabling robust learning without requiring explicit paired negative data while maintaining the contrastive goodness-based objective.