JetBrains Research unites scientists working in challenging new disciplines

Deep Image Prior

Deep convolutional networks have become a popular tool for image generation and restoration. Generally, their excellent performance is imputed to their ability to learn realistic image priors from a large number of example images.

On the contrary, the recent paper shows that the structure of generator CNN itself is sufficient to capture a great deal of low-level image statistics.

On the seminar, we will describe how a randomly-initialized neural network can be used as a handcrafted prior with excellent results in standard inverse problems such as denoising, super-resolution, and inpainting.

The whole thing indeed works like magic and proves the importance of developing a good networks architectures, which go against the common narrative that explain the success of deep learning in image restoration.

Speakers: Stanislav Belyaev.

Presentation language: Еnglish.

Date and time: October 9th, 20:30-22:00.

Location: Times, white boards (4th floor).