Nvidia corporation (20240303494). METHOD FOR FEW-SHOT UNSUPERVISED IMAGE-TO-IMAGE TRANSLATION simplified abstract
Contents
- 1 METHOD FOR FEW-SHOT UNSUPERVISED IMAGE-TO-IMAGE TRANSLATION
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 METHOD FOR FEW-SHOT UNSUPERVISED IMAGE-TO-IMAGE TRANSLATION - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Questions about Image-to-Image Translation
- 1.11 Original Abstract Submitted
METHOD FOR FEW-SHOT UNSUPERVISED IMAGE-TO-IMAGE TRANSLATION
Organization Name
Inventor(s)
Ming-Yu Liu of Redwood City CA (US)
Xun Huang of Pittsburgh PA (US)
Tero Tapani Karras of Helsinki (FI)
Jaakko Lehtinen of Helsinki (FI)
METHOD FOR FEW-SHOT UNSUPERVISED IMAGE-TO-IMAGE TRANSLATION - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240303494 titled 'METHOD FOR FEW-SHOT UNSUPERVISED IMAGE-TO-IMAGE TRANSLATION
Simplified Explanation
The patent application describes a few-shot, unsupervised image-to-image translation algorithm called "funit" that can translate images of previously unseen target classes with just a few input images.
- Accepts images of new target classes with only a few input images specified at inference time.
- Trained on a dataset containing images of various object classes to translate images from one class to another using few input images of the target class.
- Learns to extract appearance patterns from the few input images to create a generalizable appearance pattern extractor for translating images of unseen classes.
Key Features and Innovation
- Few-shot, unsupervised image-to-image translation algorithm.
- Ability to translate images of previously unseen target classes with minimal input images.
- Generalizable appearance pattern extractor learned from few input images for translation tasks.
Potential Applications
- Art and design applications for quick image translation.
- E-commerce for product image translation.
- Augmented reality for real-time image transformation.
Problems Solved
- Overcoming the need for large amounts of training data for image translation.
- Enabling translation of images of new target classes with minimal input images.
Benefits
- Efficient and accurate image translation with minimal input.
- Generalizable appearance pattern extractor for various translation tasks.
- Versatile application in different industries for image transformation.
Commercial Applications
- "Funit" algorithm can be used in various industries such as art, e-commerce, and augmented reality for quick and accurate image translation tasks, enhancing user experience and efficiency.
Questions about Image-to-Image Translation
How does the "funit" algorithm improve image translation efficiency?
The "funit" algorithm improves image translation efficiency by learning a generalizable appearance pattern extractor from just a few input images, enabling accurate translation of images of unseen classes with minimal data.
What are the potential applications of few-shot image-to-image translation algorithms in different industries?
Few-shot image-to-image translation algorithms like "funit" have applications in art, e-commerce, and augmented reality for quick and accurate image transformation tasks, enhancing user experience and efficiency.
Original Abstract Submitted
a few-shot, unsupervised image-to-image translation (“funit”) algorithm is disclosed that accepts as input images of previously-unseen target classes. these target classes are specified at inference time by only a few images, such as a single image or a pair of images, of an object of the target type. a funit network can be trained using a data set containing images of many different object classes, in order to translate images from one class to another class by leveraging few input images of the target class. by learning to extract appearance patterns from the few input images for the translation task, the network learns a generalizable appearance pattern extractor that can be applied to images of unseen classes at translation time for a few-shot image-to-image translation task.
