View source for Intel corporation (20240112460). APPARATUS, METHOD, AND COMPUTER-READABLE MEDIUM FOR ROBUST RESPONSE TO ADVERSARIAL PERTURBATIONS USING HYPERDIMENSIONAL VECTORS simplified abstract

=APPARATUS, METHOD, AND COMPUTER-READABLE MEDIUM FOR ROBUST RESPONSE TO ADVERSARIAL PERTURBATIONS USING HYPERDIMENSIONAL VECTORS=


==Organization Name==

[[:Category:intel corporation|intel corporation]]


[[Category:intel corporation]]

==Inventor(s)==

[[:Category:Narayan Srinivasa of San Jose CA (US)|Narayan Srinivasa of San Jose CA (US)]][[Category:Narayan Srinivasa of San Jose CA (US)]]

==APPARATUS, METHOD, AND COMPUTER-READABLE MEDIUM FOR ROBUST RESPONSE TO ADVERSARIAL PERTURBATIONS USING HYPERDIMENSIONAL VECTORS - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240112460 titled 'APPARATUS, METHOD, AND COMPUTER-READABLE MEDIUM FOR ROBUST RESPONSE TO ADVERSARIAL PERTURBATIONS USING HYPERDIMENSIONAL VECTORS

==Simplified Explanation==

The abstract describes a patent application for a method and apparatus that assigns hyperdimensional vector values to locations and pixel intensities in an image patch.

* Processor circuitry assigns a location value hyperdimensional vector (HDV) to a location in an image patch.
* At least a first channel HDV is assigned to the patch, and pixel intensity values HDVs are determined for each pixel in the patch.
* Pixel intensity value HDVs are bound together to create patch intensity value HDVs.
* The first channel HDV and patch intensity value HDVs are combined to produce a patch consensus intensity HDV.
* A first hyperdimensional representation patch value HDV is generated by binding together a combination of the patch consensus intensity HDV and the location value HDV.

== Potential Applications ==
This technology could be applied in image processing, pattern recognition, and computer vision applications.

== Problems Solved ==
This technology helps in accurately representing and analyzing image patches, improving the efficiency of image processing algorithms.

== Benefits ==
The benefits of this technology include enhanced image analysis capabilities, improved pattern recognition accuracy, and increased efficiency in processing image data.

== Potential Commercial Applications ==
Potential commercial applications of this technology include in the fields of computer vision, artificial intelligence, and image recognition software.

== Possible Prior Art ==
One possible prior art could be the use of hyperdimensional vectors in image processing and pattern recognition algorithms.

=== Unanswered Questions ===

=== How does this technology compare to existing image processing methods? ===
This article does not provide a direct comparison to existing image processing methods, leaving the reader to wonder about the specific advantages and limitations of this new approach.

=== What are the potential limitations or challenges in implementing this technology in real-world applications? ===
The article does not address the potential challenges or limitations that may arise when implementing this technology in practical settings, leaving room for speculation on the feasibility and scalability of the proposed method.


==Original Abstract Submitted==

apparatuses, methods, and articles of manufacture are disclosed. an example apparatus includes processor circuitry to assign a location value hyperdimensional vector (hdv) to a location in an image of a first patch of one or more pixels, assign at least a first channel hdv to the first patch, determine at least one pixel intensity value hdv for each of the one or more pixels in the first patch, bind together each of the pixel intensity value hdvs into at least one patch intensity value hdv, bind together the at least first channel hdv and the at least one patch intensity value hdv to produce a patch consensus intensity hdv, and generate a first hyperdimensional representation patch value hdv of the first patch by binding together at least a combination of the patch consensus intensity hdv and the location value hdv.



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