Denoising

A deep dive into the motivation behind the denoising diffusion model and detailed derivations for the loss functionPhoto by Chaozzy Lin on UnsplashThe Denoising Diffusion Probabilistic Models by Jonathan Ho et. al. is a great paper. But I had difficulty understanding it. My colleagues told me they were also left confused after reading it. So I decided to dive into the model and worked out all the derivations. In this article, I will focus on the two main obstacles to understand the paper:why the denoising diffusion model…

What’s new in Graph ML?Is Denoising Diffusion all you need?The breakthrough in Denoising Diffusion Probabilistic Models (DDPM) happened about 2 years ago. Since then, we observe dramatic improvements in generation tasks: GLIDE, DALL-E 2, Imagen, Stable Diffusion for images, Diffusion-LM in language modeling, diffusion for video sequences, and even diffusion for reinforcement learning.Diffusion might be the biggest trend in GraphML in 2022 — particularly when applied to drug discovery, molecules and conformer generation,…

A novel approach for denoising medical imagesSourceDeep learning, especially Convolutional Neural Networks (CNNs), is shaping the future of data-driven problem solving. From text-related problems like speech generation, content writing, etc to vision tasks like image classification, object detection, CNNs are widely used. In the past few years, numerous advanced CNN architectures have been proposed like Graph CNNs, Attention-based CNNs, Complex-valued CNNs etc. In this article I will be summarizing my research paper…

Image Denoising with No Data and a Random NetworkFigure 1. DIP Pipeline. A single image is used for training, and the aim is to reconstruct the image from the noise. Eventually the network learns to reconstruct a denoised version of the image. Image created by author.Deep learning and neural networks have been tightly associated with big data. Whether it is image classification of language translation, you almost always require a vast quantity of data to boost the task accuracy for the model to be applicable to real-world…

How to tackle inherent interferences of radar satellitesA Sentinel satellite in orbit, image by Rama, Wikimedia CommonsBy Emanuele Dalsasso (researcher at CNAM and Telecom Paris), Youcef Kemiche (Hi! Paris machine learning engineer), Pierre Blanchard (Hi! Paris machine learning engineer)When people think about satellite imagery, they usually think of pictures showing massive hurricanes above continents. This kind of images are captured by optical sensors and are widely used by scientists to measure and anticipate forest…