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It seems like every few months, someone publishes a machine learning paper or demo that makes my jaw drop. This month, it’s OpenAI’s new image-generating model, DALL·E.
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.
A successor capable of generating more complex and realistic images, DALL-E 2, was unveiled in April 2022, [11] followed by Stable Diffusion that was publicly released in August 2022. [12] In August 2022, text-to-image personalization allows to teach the model a new concept using a small set of images of a new object that was not included in ...
Multimodal learning is a type of deep learning that integrates and processes multiple types of data, referred to as modalities, such as text, audio, images, or video.This integration allows for a more holistic understanding of complex data, improving model performance in tasks like visual question answering, cross-modal retrieval, [1] text-to-image generation, [2] aesthetic ranking, [3] and ...
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On the other side, synthetic data is often used as an alternative to data produced by real-world events. Such data can be deployed to validate mathematical models and to train machine learning models while preserving user privacy, [188] including for structured data. [189]
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