Nvidia corporation (20240185396). VISION TRANSFORMER FOR IMAGE GENERATION simplified abstract
Contents
- 1 VISION TRANSFORMER FOR IMAGE GENERATION
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 VISION TRANSFORMER FOR IMAGE GENERATION - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
VISION TRANSFORMER FOR IMAGE GENERATION
Organization Name
Inventor(s)
Ali Hatamizadeh of Los Angeles CA (US)
Jiaming Song of San Carlos CA (US)
Jan Kautz of Lexington MA (US)
Arash Vahdat of Mountain View CA (US)
VISION TRANSFORMER FOR IMAGE GENERATION - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240185396 titled 'VISION TRANSFORMER FOR IMAGE GENERATION
Simplified Explanation
The patent application describes apparatuses, systems, and techniques to generate images using machine learning models that calculate attention scores using time embeddings.
- Machine learning models are used to generate output images based on attention scores calculated using time embeddings.
- The technology involves the use of time embeddings to enhance the generation of images.
Potential Applications
This technology could be applied in various fields such as medical imaging, satellite imaging, and video processing.
Problems Solved
This technology helps improve the accuracy and efficiency of image generation by incorporating time embeddings in the calculation of attention scores.
Benefits
The use of machine learning models and time embeddings can lead to more precise and detailed image generation, benefiting industries that rely on high-quality images.
Potential Commercial Applications
This technology could be valuable in industries such as healthcare, surveillance, and entertainment for enhancing image generation processes.
Possible Prior Art
Prior art may include patents related to image generation using machine learning models and attention mechanisms, as well as patents involving the use of time embeddings in image processing.
Unanswered Questions
How does this technology compare to existing image generation methods using machine learning models and attention mechanisms?
This article does not provide a direct comparison between this technology and existing methods, leaving the reader to wonder about the specific advantages and limitations of this approach.
What are the specific industries or applications that could benefit the most from this technology?
While the article mentions potential applications in various fields, it does not delve into the specific industries or use cases that could see the greatest impact from implementing this technology.
Original Abstract Submitted
apparatuses, systems, and techniques to generate images. in at least one embodiment, one or more machine learning models generate an output image based, at least in part, on calculating attention scores using time embeddings.
