Dell Patent for Training Image Processing Model
Summary
The USPTO has granted Dell Products L.P. a patent (US12586152B2) for a method, electronic device, and computer program product for training an image processing model. The patent details a process involving folding weights of convolutional layers and training the model with sample images of varying resolutions.
What changed
The United States Patent and Trademark Office (USPTO) has granted Dell Products L.P. patent US12586152B2, titled 'Method, electronic device, and computer program product for training image processing model.' The patent, effective March 24, 2026, covers a novel method for training image processing models. Key aspects include obtaining folding weights from convolutional layers of a pre-trained generator via a folding operation and embedding this generator into the image processing model. The training process utilizes pairs of sample images with different resolutions, specifically including a first sample image with a lower resolution and a second sample image with a higher resolution.
This patent grant represents a new intellectual property for Dell related to AI and machine learning in image processing. While it does not impose direct compliance obligations on other entities, it signifies innovation in the field and may influence future product development and licensing strategies for companies involved in AI-driven image analysis. Companies developing or utilizing similar image processing models should be aware of this patent's existence and scope, particularly concerning the described training methodologies involving resolution variations and generator embedding.
Source document (simplified)
Method, electronic device, and computer program product for training image processing model
Grant US12586152B2 Kind: B2 Mar 24, 2026
Assignee
Dell Products L.P.
Inventors
Zijia Wang, Zhisong Liu, Zhen Jia
Abstract
Embodiments of the present disclosure relate to a method, an electronic device, and a computer program product for training an image processing model. The method in an illustrative embodiment includes: obtaining a folding weight of a folded convolutional layer of a pre-trained generator by performing a folding operation on a plurality of weights of a plurality of convolutional layers of the pre-trained generator. The method further includes: embedding the pre-trained generator into the image processing model. The method further includes: training the image processing model using a plurality of pairs of sample images, wherein at least one pair of sample images of the plurality of pairs of sample images includes a first sample image having a first resolution and a second sample image having a second resolution, and wherein the first resolution is less than the second resolution.
CPC Classifications
G06N 3/0464 G06N 3/04 G06N 3/045 G06N 3/047 G06N 3/08 G06N 3/084 G06N 3/088 G06N 3/094 G06T 3/4046 G06T 11/00
Filing Date
2023-04-03
Application No.
18130022
Claims
20
Named provisions
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