A weighted, layer-wise annealed dropout applied at intermediate layers of blind super-resolution networks improves generalization on unseen degradations over prior regularization methods.
Revisiting resnets: Improved training and scaling strategies.Advances in Neural Information Process- ing Systems, 34:22614–22627, 2021
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Adaptive Dropout: Unleashing Dropout across Layers for Generalizable Image Super-Resolution
A weighted, layer-wise annealed dropout applied at intermediate layers of blind super-resolution networks improves generalization on unseen degradations over prior regularization methods.