YA's Blog

Diffusion models are a new class of state-of-the-art generative models that generate diverse high-resolution images. There are already a bunch of different diffusion models that include Open AI’s DALL-E 2 and GLIDE, Google’s Imagen, and Stability AI’s Stable Diffusion. In this blog post, we will dig our way up from the basic principles described in the most prominent one, which is the Denoising Diffusion Probabilistic Models (DDPM) as initialized by Sohl-Dickstein et al in 2015 and then improved by Ho. et al in 2020. Images produced by Dall-E 2 The basic idea behind diffusion models is rather simple. It takes an input image $\mathbf{x}_0$ and gradually adds Gaussian noise to it through a series of $T$ time steps. We will call this the forward process. A network is then trained to recover the original image by reversing the noising process. By being able to model the reverse process, we can start from random noise and denoise it step-by-step to generate new data. Forward Diffus

In 2015, Batch Normalization was discovered, which heralded the progress of architectures as it was now possible to train deep networks without using any tricks - allowing us to use much higher learning rates and be less careful about initialization. It also acts as a regularizer, eliminating the need for Dropout. Another major problem with deep networks was of vanishing/exploding gradients, but it was now being handled by Kaiming Initialization. With the stage set in place, experiments began on training deeper models. It was noted that when deeper networks are able to start converging, a degradation problem occurs: as we increase the depth, the accuracy gets saturated (which might be unsurprising) and then degrades rapidly. Unexpectedly, such degradation is not caused by overfitting (which was the initial guess), because adding more layers to a suitably deep model lead to higher training error. It turns out, deeper networks are underfitting ! By intuition, it is expect

The 2014 winner of the ImageNet challenge was the InceptionNet or GoogLeNet architecture from Google. The most straightforward way of improving the performance of deep neural networks is by increasing their size: depth (the number of layers) and width (the number of units at each layer). But with such a big network comes a large number of parameters (which makes it prone to overfitting) and increased use of computation resources. In order to cater to these problems, the design choices of GoogLeNet were focused on efficiency. Architecture: In order to very aggressively downsample the spatial dimensions of the input image at the beginning, GoogLeNet uses a Stem Network. This looks very similar to AlexNet or ZFNet (and local response normalization is used in this network). It then includes an Inception module, a local structure with parallel branches that was repeated many times throughout the entire network (just like stages in VGGNet). In order to eliminate the