公开(公告)号：US20250095347A1

公开(公告)日：2025-03-20

申请号：US18293315

申请日：2022-08-24

Applicant: Arizona Board of Regents on behalf of Arizona State University

Inventor： Roy Asim

IPC: G06V10/82 ,  G06V10/778

Abstract: Described herein are means for systematically generating transparent models for computer vision and image recognition utilizing deep learning non-transparent black box models. According to a particular embodiment, there is a specially configured system for generating an explainable AI model by performing operations, including: training a Convolutional Neural Network (CNN) to classify objects; training a Convolutional Neural Network (CNN) to classify objects from training data having a set of training images; training a multi-layer perceptron (MLP) to recognize both the objects and parts of the objects; generating the explainable AI model based on the training of the MLP; receiving an image having an object embedded therein, wherein the image forms no portion of the training data for the explainable AI model; executing the CNN and the explainable AI model within an image recognition system, and generating a prediction of the object in the image via the explainable AI model; recognizing parts of the object; providing the parts recognized within the object as evidence for the prediction of the object; and generating a description of why the image recognition system predicted the object in the image based on the evidence comprising the recognized parts.