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Found 2 papers, 2 papers with code
Invertibility of Convolutional Generative Networks from Partial Measurements
In this work, we present new theoretical results on convolutional generative neural networks, in particular their invertibility (i. e., the recovery of input latent code given the network output).
Sparse Depth Sensing for Resource-Constrained Robots
We address the following question: is it possible to reconstruct the geometry of an unknown environment using sparse and incomplete depth measurements?
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