TY - GEN
T1 - Image-adaptive gan based reconstruction
AU - Hussein, Shady Abu
AU - Tirer, Tom
AU - Giryes, Raja
N1 - Publisher Copyright:
© 2020, Association for the Advancement of Artificial Intelligence.
PY - 2020
Y1 - 2020
N2 - In the recent years, there has been a significant improvement in the quality of samples produced by (deep) generative models such as variational auto-encoders and generative adversarial networks. However, the representation capabilities of these methods still do not capture the full distribution for complex classes of images, such as human faces. This deficiency has been clearly observed in previous works that use pre-trained generative models to solve imaging inverse problems. In this paper, we suggest to mitigate the limited representation capabilities of generators by making them image-adaptive and enforcing compliance of the restoration with the observations via back-projections. We empirically demonstrate the advantages of our proposed approach for image super-resolution and compressed sensing.
AB - In the recent years, there has been a significant improvement in the quality of samples produced by (deep) generative models such as variational auto-encoders and generative adversarial networks. However, the representation capabilities of these methods still do not capture the full distribution for complex classes of images, such as human faces. This deficiency has been clearly observed in previous works that use pre-trained generative models to solve imaging inverse problems. In this paper, we suggest to mitigate the limited representation capabilities of generators by making them image-adaptive and enforcing compliance of the restoration with the observations via back-projections. We empirically demonstrate the advantages of our proposed approach for image super-resolution and compressed sensing.
UR - http://www.scopus.com/inward/record.url?scp=85094613574&partnerID=8YFLogxK
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AN - SCOPUS:85094613574
T3 - AAAI 2020 - 34th AAAI Conference on Artificial Intelligence
SP - 3121
EP - 3129
BT - AAAI 2020 - 34th AAAI Conference on Artificial Intelligence
PB - AAAI press
T2 - 34th AAAI Conference on Artificial Intelligence, AAAI 2020
Y2 - 7 February 2020 through 12 February 2020
ER -