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Found 3 papers, 2 papers with code
Learned residual Gerchberg-Saxton network for computer generated holography
Instead of an iterative optimization algorithm that converges to a (sub-)optimal solution, the inverse problem can be solved by training a neural network to directly estimate the inverse operator.
On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset
Learning meaningful and compact representations with disentangled semantic aspects is considered to be of key importance in representation learning.
Automatic Estimation of Modulation Transfer Functions
The modulation transfer function (MTF) is widely used to characterise the performance of optical systems.
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