This is my 4th note in Diffusion models. For the previous notes, please refer to diffusion and stable diffusion. My contents are based on paper listed in Diffusion Explained and Diffusion Models: A Comprehensive Survey of Methods and Applications.

This is my reading note on Hierarchical Text-Conditional Image Generation with CLIP Latents. This paper proposes a two-stage model (unCLIP): a prior that generates a CLIP image embedding given a text caption, and a decoder that generates an image conditioned on the image embedding, for generating images from text.

This is my 2nd reading note on diffusion model, which will focus on the stabe diffusion, aka High-Resolution Image Synthesis with Latent Diffusion Models. By decomposing the image formation process into a sequential application of denoising autoencoders, diffusion models (DMs) achieve state-of-the-art synthesis results on image data and beyond. However, as mentioned in diffusion, DM sufferes high computational cost. The proposed Latent Diffusion Models (LDM) reduces the computational cost via latent space and introduces cross-attention to enable multi-modality conditioning.

This document describes the specifications of typical human vision system: resolution 576 megapixels with eye movement or 324 megapixels at a single glint; angle of view is around 180°; light sensitivity is about ISO 800; dynamic range is 1 billion to 1 with adjustment or 10000 to 1 with a single glint; focal length at 22mm and aperture size at F/3.2.

This is my 1st reading note of on recent progress of difussion model. It is based on Diffusion Models: A Comprehensive Survey of Methods and Applications. Diffusion probabilistic models were originally proposed as a latent variable generative model inspired by non- equilibrium thermodynamics. The essential idea of diffusion models is to systematically perturb the structure in a data distribution through a forward diffusion process, and then recover the structure by learning a reverse diffusion process, resulting in a highly flexible and tractable generative model.

Neural Radiance Field (NeRF), you may have heard words many times for the past few months. Yes, this is the latest progress of neutral work and computer graphics. NeRF represents a scene with learned, continuous volumetric radiance field \(F_{\theta}\) defined over a bounded 3D volume. In Nerf, \(F_{\theta}\) is a multilayer perceptron (MLP) that takes as input a 3D position \(x=(x,y,z)\) and unit-norm viewing direction \(d=(d_x,d_y,d_z)\), and produces as output a density \(\sigma\) and color \(c=(r,g,b)\). By enumerating all most position and direction for a bounded 3D volumne, we could obtain the 3D scene.

Convolutional Neural Networks (ConvNets or CNNs) are a class of neural networks algorithms that are mostly used in visual recognition tasks such as image classification, object detection, and image segmentation. The use of ConvNets in visual recognition is inarguably one of the biggest inventions of decade 2010s in deep learning community.

This post is my review of house search from Sep to Oct of 2021. I have checked 88 houses and most of which I have paid a visit. I was looking for a new house as my office has been moved to north and the long commute from my current house makes me to move somewhere north. As a result, I mainly considered the area from Mountain View (north of 85) to Belmont.

This is my reading note on CVPR 2021 tutorial on self supervised learning: Leave Those Nets Alone: Advances in Self-Supervised Learning and Data- and Label-Efficient Learning in An Imperfect World.

This is my reading note on CVPR 2021 Tutorial: Data- and Label-Efficient Learning in An Imperfect World. The original slides and videos are available online. Unsupervised domain adaption methods could be divided into the following groups: