Gradient-Driven Diffusion Learning Model
A Gradient-Driven Diffusion Learning Model is a Generative Machine Learning model that ...
- Example(s):
- a Stable Diffusion.
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- Counter-Example(s):
- See: DALL-E 2 Model, Latent Variable Model, Markov Chain, Variational Bayesian Methods, Gaussian Noise, Image Denoising, Inpainting, Reaction-Diffusion Equation, Drift-diffusion model.
References
2023
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- Autoregressive models and diffusion models are two types of machine learning models that are used for different purposes and have different characteristics.
Autoregressive models are a type of statistical model that predict the next value in a sequence based on the previous values in the sequence. They are often used for time series forecasting and natural language processing tasks. Autoregressive models are typically implemented as feedforward neural networks, where the input at each time step is the previous time step's output. The model uses the previous values in the sequence as input to predict the next value. Autoregressive models have a linear time complexity with respect to the sequence length, and the model can only be trained on one example at a time.
Diffusion models, on the other hand, are a type of generative model that are used to generate new samples from a given probability distribution. They are often used in unsupervised learning tasks, such as density estimation and generative modeling. Diffusion models are implemented as recurrent neural networks, where the input at each time step is the previous time step's output and a random noise sample. The model uses the random noise to explore different areas of the probability distribution and gradually converge to a good representation of the target distribution. Diffusion models have a quadratic time complexity with respect to the sequence length, and the model can be trained on multiple examples in parallel.
With respect to encoding, Autoregressive models encode the information in a sequence in a deterministic way, where the next element of the sequence is completely determined by the previous elements. On the other hand, Diffusion models encode the information in a sequence in a probabilistic way, where the next element of the sequence is determined by a random noise sample, which is combined with the previous elements to produce the next element.
In terms of parallelism, Autoregressive models are inherently sequential since they are predicting the next value based on the previous values. Therefore, they are not easily parallelizable and the training process requires a sequential processing of the data. Diffusion models, however, can be easily parallelized, since the model can be trained on multiple examples at the same time, and the model's output at each time step is independent of the other time steps.
- Autoregressive models and diffusion models are two types of machine learning models that are used for different purposes and have different characteristics.
2023
- (Wikipedia, 2023) ⇒ https://en.wikipedia.org/wiki/Diffusion_model Retrieved:2023-1-17.
- In machine learning, diffusion models, also known as diffusion probabilistic models, are a class of latent variable models. They are Markov chains trained using variational inference. The goal of diffusion models is to learn the latent structure of a dataset by modeling the way in which data points diffuse through the latent space. In computer vision, this means that a neural network is trained to denoise images blurred with Gaussian noise by learning to reverse the diffusion process. Diffusion models were introduced in 2015 with a motivation from non-equilibrium thermodynamics. Diffusion models can be applied to a variety of tasks, including image denoising, inpainting, super-resolution, and image generation. For example, an image generation model would start with a random noise image and then, after having been trained reversing the diffusion process on natural images, the model would be able to generate new natural images. Announced on 13 April 2022, OpenAI's text-to-image model DALL-E 2 is a recent example. It 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.