Diffusion model

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In computer vision, image generation, and natural language processing, diffusion models are fundamental tools. They are utilized for functions such as image denoising, inpainting, super-resolution, and text generation, with the capability to train for the removal of Gaussian noise in blurred images. Notable examples of such models encompass denoising diffusion probabilistic models and noise conditioned score networks. In non-equilibrium thermodynamics, these models are instrumental in sampling from intricate probability distributions. They are further optimized through advanced methodologies like variational inference[1] and stochastic gradient descent. In natural language processing, diffusion models are instrumental for text generation and summarization, mastering the hidden structure of text data to yield contextually appropriate text. Renowned research entities like OpenAI and Google[2] Imagen have pioneered various diffusion models for tasks related to image and text generation.

Terms definitions
1. inference. Inference, a mental process, entails forming conclusions from existing evidence and logical reasoning. It's an integral aspect of critical thinking and problem-solving, with wide-ranging applications in areas such as scientific investigation, literary analysis, and artificial intelligence. Various forms of inference exist, such as deductive, inductive, abductive, statistical, and causal, each with its distinctive method and purpose. For example, deductive inference focuses on reaching specific conclusions from broad principles, whereas inductive inference generates broad conclusions from specific instances. Conversely, abductive inference involves making informed assumptions based on accessible evidence, while statistical and causal inferences revolve around interpreting data to make conclusions about a group or to establish cause-and-effect connections. Nonetheless, the precision of inferences can be affected by biases, preconceived notions, and misinterpretations. Despite these potential obstacles, enhancing inference skills is achievable through consistent practice, critical thinking activities, and exposure to a variety of reading materials.
2. Google ( Google ) Primarily acknowledged for its search engine, Google is a universally esteemed technology corporation. The company, established in 1998 by Sergey Brin and Larry Page, has expanded significantly, branching out into numerous tech-related fields. Google offers a wide array of services and products, encompassing Android, YouTube, Cloud, Maps, and Gmail. It also manufactures hardware like Chromebooks and Pixel smartphones. Since 2015, Google has been a subsidiary of Alphabet Inc. and is celebrated for its inventive spirit and workplace environment that promotes employees' personal projects. Despite confronting several ethical and legal challenges, Google continues to influence the tech sector with its groundbreaking innovations and technological progress, including the creation of Android OS and the purchase of companies specializing in AI.
Diffusion model (Wikipedia)

In machine learning, diffusion models, also known as diffusion probabilistic models or score-based generative models, are a class of latent variable generative models. A diffusion model consists of three major components: the forward process, the reverse process, and the sampling procedure. The goal of diffusion models is to learn a diffusion process that generates a probability distribution for a given dataset from which we can then sample new images. They learn the latent structure of a dataset by modeling the way in which data points diffuse through their latent space.

In the case of computer vision, diffusion models can be applied to a variety of tasks, including image denoising, inpainting, super-resolution, and image generation. This typically involves training a neural network to sequentially denoise images blurred with Gaussian noise. The model is trained to reverse the process of adding noise to an image. After training to convergence, it can be used for image generation by starting with an image composed of random noise for the network to iteratively denoise. Announced on 13 April 2022, OpenAI's text-to-image model DALL-E 2 is an example that uses diffusion models for both the model's prior (which produces an image embedding given a text caption) and the decoder that generates the final image. Diffusion models have recently found applications in natural language processing (NLP), particularly in areas like text generation and summarization.

Diffusion models are typically formulated as markov chains and trained using variational inference. Examples of generic diffusion modeling frameworks used in computer vision are denoising diffusion probabilistic models, noise conditioned score networks, and stochastic differential equations.

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