David Alexander (@david-alexander.bsky.social)

AI predictions of #LandCover in the #PeakDistritct #NationalPark #GIS #maps https://www.mdpi.com/2072-4292/15/22/5277

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. In this paper, we show that, on the contrary, the structure of a generator network is sufficient to capture a great deal of low-level image statistics prior to any learning. In order to do so, we show that 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. Furthermore, the same prior can be used to invert deep neural representations to diagnose them, and to restore images based on flash-no flash input pairs. Apart from its diverse applications, our approach highlights the inductive bias captured by standard generator network architectures. It also bridges the gap between two very popular families of image restoration methods: learning-based methods using deep convolutional networks and learning-free methods based on handcrafted image priors such as self-similarity. Code and supplementary material are available at https://dmitryulyanov.github.io/deep_image_prior .

Google’s Augmented Reality Microscope Might Help Diagnose Cancer

Despite recent advances in diagnosing cancer, many cases are still diagnosed using biopsies and analyzing thin slices of tissue underneath a microscope. Properly analyzing these tissue sample slide…

AI algorithm learns microscopic details of nematicity in moiré systems

Identifying and understanding experimental signatures of phases of matter is usually a challenging task due to strong electron interactions in a material and can become even harder due to external influences in samples with the presence of impurities or other sources of deformations. Typically, these interactions between the electrons in a material give rise to fascinating phenomena such as magnetism, superconductivity and electronic nematicity.

Teaching A Mini-Tesla To Steer Itself

At the risk of stating the obvious, even when you’ve got unlimited resources and access to the best engineering minds, self-driving cars are hard. Building a multi-ton guided missile that can…

Area Estimation of Mango and Coconut Crops using Machine Learning in Hesaraghatta Hobli of Bengaluru Urban District, Karnataka | Journal of Geomatics