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A text-to-image model is a machine learning model which takes an input natural language description 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 ...
Search engine indexing is the collecting, parsing, and storing of data to facilitate fast and accurate information retrieval. Index design incorporates interdisciplinary concepts from linguistics, cognitive psychology, mathematics, informatics, and computer science. An alternate name for the process, in the context of search engines designed to ...
Image2Text. Image2Text technology was created by Cortica, an Israel-based startup whose technology simulates the performance of the human cortex so that computers recognize images with a high degree of accuracy. [1] Image2Text is the result of 10 years in research and development and is protected by more than 50 patents.
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Most image editing software is capable of importing and exporting in a variety of formats though, and a number of dedicated image converters exist. Loss due to compression [ edit ] Besides uncompressed formats and lossless compression formats that can usually be interconverted without any loss of detail, there are compressed formats such as ...
Stable Diffusion. Stable Diffusion is a deep learning, text-to-image model released in 2022 based on diffusion techniques. The generative artificial intelligence technology is the premier product of Stability AI and is considered to be a part of the ongoing artificial intelligence boom .
Text-to-image personalization. Text-to-Image personalization is a task in deep learning for computer graphics that augments pre-trained text-to-image generative models. In this task, a generative model that was trained on large-scale data (usually a foundation model ), is adapted such that it can generate images of novel, user-provided concepts.
An image segmentation neural network can process small areas of an image to extract simple features such as edges. [81] Another neural network, or any decision-making mechanism, can then combine these features to label the areas of an image accordingly. A type of network designed this way is the Kohonen map.
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