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Text-to-image Generation
A text-to-image model is a machine learning model which takes an input natural language prompt and produces an image matching that description. Text-to-image models began to be developed in the mid-2010s during the beginnings of the AI boom, as a result of advances in deep neural networks. In 2022, the output of state-of-the-art text-to-image models—such as OpenAI's DALL-E 2, Google Brain's Imagen, Stability AI's Stable Diffusion, and Midjourney—began to be considered to approach the quality of real photographs and human-drawn art. Text-to-image models are generally latent diffusion models, which combine a language model, which transforms the input text into a latent representation, and a generative image model, which produces an image conditioned on that representation. The most effective models have generally been trained on massive amounts of image and text data scraped from the web. History Before the rise of deep learning, attempts to build text-to-im ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Astronaut Riding A Horse Hiroshige (SD3
An astronaut (from the Ancient Greek (), meaning 'star', and (), meaning 'sailor') is a person trained, equipped, and deployed by a List of human spaceflight programs, human spaceflight program to serve as a commander or crew member of a spacecraft. Although generally reserved for professional space travelers, the term is sometimes applied to anyone who travels into space, including scientists, politicians, journalists, and space tourists. "Astronaut" technically applies to all human space travelers regardless of nationality. However, astronauts fielded by Russia or the Soviet Union are typically known instead as cosmonauts (from the Russian "kosmos" (космос), meaning "space", also borrowed from Greek ). Comparatively recent developments in crewed spaceflight made by China have led to the rise of the term taikonaut (from the Standard Chinese, Mandarin "tàikōng" (), meaning "space"), although its use is somewhat informal and its origin is unclear. In China, the People' ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Collage
Collage (, from the , "to glue" or "to stick together") is a technique of art creation, primarily used in the visual arts, but in music too, by which art results from an assembly of different forms, thus creating a new whole. (Compare with pastiche, which is a "pasting" together.) Collage may refer to the technique as a whole, or more specifically to a two-dimensional work, assembled from flat pieces on a flat substrate, whereas Assemblage (art), assemblage typically refers to a three-dimensional equivalent. A collage may sometimes include Clipping (publications), magazine and newspaper clippings, ribbons, paint, bits of colored or handmade papers, portions of other artwork or texts, photographs and other found objects, glued to a piece of paper or canvas. The origins of collage can be traced back hundreds of years, but this technique made a dramatic reappearance in the early 20th century as an art form of novelty. The term ''Papier collé'' was coined by both Georges Braque a ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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OpenAI
OpenAI, Inc. is an American artificial intelligence (AI) organization founded in December 2015 and headquartered in San Francisco, California. It aims to develop "safe and beneficial" artificial general intelligence (AGI), which it defines as "highly autonomous systems that outperform humans at most economically valuable work". As a leading organization in the ongoing AI boom, OpenAI is known for the GPT family of large language models, the DALL-E series of text-to-image models, and a text-to-video model named Sora (text-to-video model), Sora. Its release of ChatGPT in November 2022 has been credited with catalyzing widespread interest in generative AI. The organization has a complex corporate structure. As of April 2025, it is led by the Nonprofit organization, non-profit OpenAI, Inc., Delaware General Corporation Law, registered in Delaware, and has multiple for-profit subsidiaries including OpenAI Holdings, LLC and OpenAI Global, LLC. Microsoft has invested US$13 billion ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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COCO (dataset)
These datasets are used in machine learning (ML) research and have been cited in peer-reviewed academic journals. Datasets are an integral part of the field of machine learning. Major advances in this field can result from advances in learning algorithms (such as deep learning), computer hardware, and, less-intuitively, the availability of high-quality training datasets. High-quality labeled training datasets for supervised and semi-supervised machine learning algorithms are usually difficult and expensive to produce because of the large amount of time needed to label the data. Although they do not need to be labeled, high-quality datasets for unsupervised learning can also be difficult and costly to produce. Many organizations, including governments, publish and share their datasets. The datasets are classified, based on the licenses, as Open data and Non-Open data. The datasets from various governmental-bodies are presented in List of open government data sites. The ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Generative Adversarial Network
A generative adversarial network (GAN) is a class of machine learning frameworks and a prominent framework for approaching generative artificial intelligence. The concept was initially developed by Ian Goodfellow and his colleagues in June 2014. In a GAN, two neural networks compete with each other in the form of a zero-sum game, where one agent's gain is another agent's loss. Given a training set, this technique learns to generate new data with the same statistics as the training set. For example, a GAN trained on photographs can generate new photographs that look at least superficially authentic to human observers, having many realistic characteristics. Though originally proposed as a form of generative model for unsupervised learning, GANs have also proved useful for semi-supervised learning, fully supervised learning, and reinforcement learning. The core idea of a GAN is based on the "indirect" training through the discriminator, another neural network that can tell ho ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Data Set
A data set (or dataset) is a collection of data. In the case of tabular data, a data set corresponds to one or more table (database), database tables, where every column (database), column of a table represents a particular Variable (computer science), variable, and each row (database), row corresponds to a given Record (computer science), record of the data set in question. The data set lists values for each of the variables, such as for example height and weight of an object, for each member of the data set. Data sets can also consist of a collection of documents or files. In the open data discipline, a dataset is a unit used to measure the amount of information released in a public open data repository. The European data.europa.eu portal aggregates more than a million data sets. Properties Several characteristics define a data set's structure and properties. These include the number and types of the attributes or variables, and various statistical measures applicable to the ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Image Scaling
In computer graphics and digital imaging, image scaling refers to the resizing of a digital image. In video technology, the magnification of digital material is known as upscaling or resolution enhancement. When scaling a vector graphic image, the graphic primitives that make up the image can be scaled using geometric transformations with no loss of image quality. When scaling a raster graphics image, a new image with a higher or lower number of pixels must be generated. In the case of decreasing the pixel number (scaling down), this usually results in a visible quality loss. From the standpoint of digital signal processing, the scaling of raster graphics is a two-dimensional example of sample-rate conversion, the conversion of a discrete signal from a sampling rate (in this case, the local sampling rate) to another. Mathematical Image scaling can be interpreted as a form of image resampling or image reconstruction from the view of the Nyquist sampling theorem. According ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Image Resolution
Image resolution is the level of detail of an image. The term applies to digital images, film images, and other types of images. "Higher resolution" means more image detail. Image resolution can be measured in various ways. Resolution quantifies how close lines can be to each other and still be visibly ''resolved''. Resolution units can be tied to physical sizes (e.g. lines per mm, lines per inch), to the overall size of a picture (lines per picture height, also known simply as lines, TV lines, or TVL), or to angular subtense. Instead of single lines, line pairs are often used, composed of a dark line and an adjacent light line; for example, a resolution of 10 lines per millimeter means 5 dark lines alternating with 5 light lines, or 5 line pairs per millimeter (5 LP/mm). Photographic lens are most often quoted in line pairs per millimeter. Types The resolution of digital cameras can be described in many different ways. Pixel count The term ''resolution'' is often considered eq ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Attention Mechanism
In machine learning, attention is a method that determines the importance of each component in a sequence relative to the other components in that sequence. In natural language processing, importance is represented b"soft"weights assigned to each word in a sentence. More generally, attention encodes vectors called token embeddings across a fixed-width sequence that can range from tens to millions of tokens in size. Unlike "hard" weights, which are computed during the backwards training pass, "soft" weights exist only in the forward pass and therefore change with every step of the input. Earlier designs implemented the attention mechanism in a serial recurrent neural network (RNN) language translation system, but a more recent design, namely the transformer, removed the slower sequential RNN and relied more heavily on the faster parallel attention scheme. Inspired by ideas about attention in humans, the attention mechanism was developed to address the weaknesses of leveraging ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Variational Autoencoder
In machine learning, a variational autoencoder (VAE) is an artificial neural network architecture introduced by Diederik P. Kingma and Max Welling. It is part of the families of probabilistic graphical models and variational Bayesian methods. In addition to being seen as an autoencoder neural network architecture, variational autoencoders can also be studied within the mathematical formulation of variational Bayesian methods, connecting a neural encoder network to its decoder through a probabilistic latent space (for example, as a multivariate Gaussian distribution) that corresponds to the parameters of a variational distribution. Thus, the encoder maps each point (such as an image) from a large complex dataset into a distribution within the latent space, rather than to a single point in that space. The decoder has the opposite function, which is to map from the latent space to the input space, again according to a distribution (although in practice, noise is rarely a ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Recurrent Neural Network
Recurrent neural networks (RNNs) are a class of artificial neural networks designed for processing sequential data, such as text, speech, and time series, where the order of elements is important. Unlike feedforward neural networks, which process inputs independently, RNNs utilize recurrent connections, where the output of a neuron at one time step is fed back as input to the network at the next time step. This enables RNNs to capture temporal dependencies and patterns within sequences. The fundamental building block of RNNs is the ''recurrent unit'', which maintains a ''hidden state''—a form of memory that is updated at each time step based on the current input and the previous hidden state. This feedback mechanism allows the network to learn from past inputs and incorporate that knowledge into its current processing. RNNs have been successfully applied to tasks such as unsegmented, connected handwriting recognition, speech recognition, natural language processing, and neural ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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University Of Toronto
The University of Toronto (UToronto or U of T) is a public university, public research university whose main campus is located on the grounds that surround Queen's Park (Toronto), Queen's Park in Toronto, Ontario, Canada. It was founded by royal charter in 1827 as King's College, the first institution of higher learning in Upper Canada. Originally controlled by the Church of England, the university assumed its present name in 1850 upon becoming a secular institution. It has three campuses: University of Toronto Mississauga, Mississauga, #St. George campus, St. George, and University of Toronto Scarborough, Scarborough. Its main campus, St. George, is the oldest of the three and located in Downtown Toronto. U of T operates as a collegiate university, comprising 11 #Colleges, colleges, each with substantial autonomy on financial and institutional affairs and significant differences in character and history. The University of Toronto is the largest university in Canada with a t ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |