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2. Word2vec - Wikipedia

   en.wikipedia.org/wiki/Word2vec

   The word with embeddings most similar to the topic vector might be assigned as the topic's title, whereas far away word embeddings may be considered unrelated. As opposed to other topic models such as LDA , top2vec provides canonical ‘distance’ metrics between two topics, or between a topic and another embeddings (word, document, or otherwise).

3. Bag-of-words model in computer vision - Wikipedia

   en.wikipedia.org/wiki/Bag-of-words_model_in...

   Take LDA for an example. To model natural scene images using LDA, an analogy is made with document analysis: the image category is mapped to the document category; the mixture proportion of themes maps the mixture proportion of topics; the theme index is mapped to topic index; the codeword is mapped to the word.

4. DALL-E - Wikipedia

   en.wikipedia.org/wiki/DALL-E

   The discrete VAE can convert an image to a sequence of tokens, and conversely, convert a sequence of tokens back to an image. This is necessary as the Transformer does not directly process image data. [22] The input to the Transformer model is a sequence of tokenized image caption followed by tokenized image patches.

5. 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.

6. Vision transformer - Wikipedia

   en.wikipedia.org/wiki/Vision_transformer

   Further, one can take a list of caption-image pairs, convert the images into strings of symbols, and train a standard GPT-style transformer. Then at test time, one can just give an image caption, and have it autoregressively generate the image. This is the structure of Google Parti. [34]

7. Flux (text-to-image model) - Wikipedia

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

   Flux (also known as FLUX.1) is a text-to-image model developed by Black Forest Labs, based in Freiburg im Breisgau, Germany. Black Forest Labs were founded by former employees of Stability AI. As with other text-to-image models, Flux generates images from natural language descriptions, called prompts.