Target Propagation
A neural network training method that assigns local target activation values to each layer, enabling each layer to independently minimize the discrepancy between its output and the assigned target. This approach avoids the chain-rule computation of global gradients.
Timeline (last 90 days)
Target propagation assigns a local target activation value to each layer, allowing each layer to independently minimize the gap between its own output and the target, avoiding global gradient chain differentiation
The Forward-Forward algorithm and target propagation are considered superior to standard backpropagation in biological plausibility, because there is no mechanism for transmitting precise backward gradient signals in the brain
All Facts (2)
Target propagation assigns a local target activation value to each layer, allowing each layer to independently minimize the gap between its own output and the target, avoiding global gradient chain differentiation
50%UnverifiedThe Forward-Forward algorithm and target propagation are considered superior to standard backpropagation in biological plausibility, because there is no mechanism for transmitting precise backward gradient signals in the brain
50%