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2. Text-to-image model - Wikipedia

   en.wikipedia.org/wiki/Text-to-image_model

   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 ...

3. Optical character recognition - Wikipedia

   en.wikipedia.org/wiki/Optical_character_recognition

   Video of the process of scanning and real-time optical character recognition (OCR) with a portable scanner. Optical character recognition or optical character reader (OCR) is the electronic or mechanical conversion of images of typed, handwritten or printed text into machine-encoded text, whether from a scanned document, a photo of a document, a scene photo (for example the text on signs and ...

4. Generative artificial intelligence - Wikipedia

   en.wikipedia.org/wiki/Generative_artificial...

   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] These models often generate output in response to specific prompts .

5. DALL-E - Wikipedia

   en.wikipedia.org/wiki/DALL-E

   DALL·E, DALL·E 2, and DALL·E 3 (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.

6. Ideogram (text-to-image model) - Wikipedia

   en.wikipedia.org/wiki/Ideogram_(text-to-image_model)

   Artificial intelligence. Ideogram is a freemium text-to-image model developed by Ideogram, Inc. using deep learning methodologies to generate digital images from natural language descriptions known as "prompts". The model is capable of generating legible text in the images compared to other text-to-image models. [1][2]

7. Transformer (deep learning architecture) - Wikipedia

   en.wikipedia.org/wiki/Transformer_(deep_learning...

   An "encoder-only" Transformer applies the encoder to map an input text into a sequence of vectors that represent the input text. This is usually used for text embedding and representation learning for downstream applications. BERT is encoder-only. They are less often used currently, as they were found to be not significantly better than ...