Image Generation is a process of using deep learning algorithms such as VAEs, GANs, and more recently Stable Diffusion, to create new images that are visually similar to real-world images. Successful platforms such as MidJourney and DALL-E have become a popular choice for anyone seeking to generate realistic images through Image Generation techniques.

Video Generation involves deep learning methods such as GANs and Video Diffusion to generate new videos by predicting frames based on previous frames. Video Generation can be used in various fields, such as entertainment, sports analysis, and autonomous driving. Video Generation can be often seen in use with Speech Generation. The models used for speech generation can be powered by Transformers. Speech Generation can be used in text-to-speech conversion, virtual assistants, and voice cloning.

Data augmentation is a process of generating new training data by applying various image transformations such as flipping, cropping, rotating, and color jittering. The goal is to increase the diversity of training data and avoid overfitting, which can lead to better performance of machine learning models.