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DALL-E, DALL-E 2, and DALL-E 3 (stylised DALL·E, and pronounced DOLL-E) are text-to-image models developed by OpenAI using deep learning methodologies to generate digital images from natural language descriptions known as prompts. The first version of DALL-E was announced in January 2021. In the following year, its successor DALL-E 2 was released.
In October of that year, Canva announced that it had raised an additional A$85 million at a valuation of A$3.2 billion and launched an enterprise product. [20] In December 2019, Canva announced Canva for Education, a free product for schools and other educational institutions intended to facilitate collaboration between students and teachers. [21]
Deep learning is a subset of machine learning that focuses on utilizing neural networks to perform tasks such as classification, regression, and representation learning.The field takes inspiration from biological neuroscience and is centered around stacking artificial neurons into layers and "training" them to process data.
Artificial intelligence (AI), in its broadest sense, is intelligence exhibited by machines, particularly computer systems.It is a field of research in computer science that develops and studies methods and software that enable machines to perceive their environment and use learning and intelligence to take actions that maximize their chances of achieving defined goals. [1]
Generative artificial intelligence (generative AI, GenAI, [1] or GAI) is a subset of artificial intelligence that uses generative models to produce text, images, videos, or other forms of data. [ 2 ] [ 3 ] [ 4 ] These models learn the underlying patterns and structures of their training data and use them to produce new data [ 5 ] [ 6 ] based on ...
Deep learning, characterized by its multi-layer structure that attempts to mimic the human brain, first came about in the 2010s and causing a significant shift in the world of AI art. [31] During the deep learning era, there are mainly these types of designs for generative art: autoregressive models, diffusion models, GANs, normalizing flows.
The plain transformer architecture had difficulty converging. In the original paper [1] the authors recommended using learning rate warmup. That is, the learning rate should linearly scale up from 0 to maximal value for the first part of the training (usually recommended to be 2% of the total number of training steps), before decaying again.
An artificial neural network is an interconnected group of nodes, inspired by a simplification of neurons in a brain.Here, each circular node represents an artificial neuron and an arrow represents a connection from the output of one artificial neuron to the input of another.
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