Name: Deep Internal Learning - Assaf Shocher
Start: 2020-12-20T09:00:00+02:00
End: 2020-12-20T10:30:00+02:00

Lecture abstract:

Deep Learning has always been divided into two phases: Training and Inference. Deep networks are mostly used with large data-sets both under supervised (Classification, Regression, etc.) or unsupervised (Autoencoders, GANs) regimes. Such networks are only applicable to the type of data they were trained for and do not exploit the internal statistics of a single datum. We introduce Deep Internal Learning; We train a signal-specific network, we do it at test-time and on the test-input only, in an unsupervised manner (no label or ground-truth). In this regime, training is a part of the inference, no additional data or prior training is taking place. I will demonstrate how we applied this framework for various challenges: Super-Resolution, Segmentation, Dehazing, Transparency-Separation, Watermark removal. I will also show how this approach can be incorporated into Generative Adversarial Networks by training a GAN on a single image.

The talk is based on the papers:
- "Zero-Shot Super-Resolution using Deep Internal Learning" (CVPR 2018)
http://www.wisdom.weizmann.ac.il/~vision/zssr
https://github.com/assafshocher/ZSSR

- "Double-DIP: Unsupervised Image Decomposition via Coupled Deep-Image-Priors" (CVPR 2018)
http://www.wisdom.weizmann.ac.il/~vision/DoubleDIP
https://github.com/yossigandelsman/DoubleDIP

- "InGAN: Capturing and Retargeting the DNA of a Natural Image"
(CVPR 2019)
http://www.wisdom.weizmann.ac.il/~vision/ingan
https://github.com/assafshocher/InGAN

- "Blind Super-Resolution Kernel Estimation using an Internal-GAN"
(NeurIPS 2019)
http://www.wisdom.weizmann.ac.il/~vision/kernelgan
https://github.com/sefibk/KernelGAN

Presenter BIO:

Assaf Shocher he a Deep Learning and Computer Vision researcher, working at Weizmann Institute of Science and previously he was working at Google Reseach .

Linkedin: https://www.linkedin.com/in/assaf-shocher-271424b7

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