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I don’t think this is overfitting. It’s really a form of leakage by the design; it seems that the loss function is a result of the outcome of both networks (cyc
by ashelmire 8y ago
I don’t think this is overfitting. It’s really a form of leakage by the design; it seems that the loss function is a result of the outcome of both networks (cyclegan is really two gans connected it seems), which means the loss gets shared and backpropagated through both networks.
I think the architecture is bad. Maybe you could stop the network from embedding extra data with some sort of normalization or max pooling at the very end of the network.
- soraki_soladead 8y agoI believe it's both. The produced images do not encode pixel-perfect details necessary for reconstruction. These details are reconstructed from the imperceptible differences in the outputs which act as markers _and_ the model's memorization of the content at those locations.
- ashelmire 8y agoAh, that's a good point.