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    SYSTEMS, METHODS, AND APPARATUSES FOR IMPLEMENTING TRANSPARENT MODELS FOR COMPUTER VISION AND IMAGE RECOGNITION UTILIZING DEEP LEARNING NON-TRANSPARENT BLACK BOX MODELS
    2.
    发明申请
    SYSTEMS, METHODS, AND APPARATUSES FOR IMPLEMENTING TRANSPARENT MODELS FOR COMPUTER VISION AND IMAGE RECOGNITION UTILIZING DEEP LEARNING NON-TRANSPARENT BLACK BOX MODELS 有权

    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.

    DEVICE, SYSTEM, AND METHOD FOR PASSIVE COLLECTION OF ATMOSPHERIC CARBON DIOXIDE
    7.
    发明申请
    DEVICE, SYSTEM, AND METHOD FOR PASSIVE COLLECTION OF ATMOSPHERIC CARBON DIOXIDE 有权

    公开(公告)号：US20250058266A1

    公开(公告)日：2025-02-20

    申请号：US18815777

    申请日：2024-08-26

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

    Inventor： Klaus LACKNER ,  Shreyans KEDIA ,  Venkatram CHOODAMANI ,  Robert PAGE

    IPC: B01D53/04

    Abstract: A device for passive collection of atmospheric carbon dioxide is disclosed. The device includes a release chamber having an opening and a sorbent regeneration system. The device also includes a capture structure coupled to the release chamber, having at least one collapsible support and a plurality of tiles spaced along the collapsible support. Each tile has a sorbent material. The capture structure is movable between a collection configuration and a release configuration. The collection configuration includes the capture structure extending upward from the release chamber to expose the capture structure to an airflow and allow the sorbent material to capture atmospheric carbon dioxide. The release configuration includes the collapsible support being collapsed and the plurality of tiles being sufficiently enclosed inside the release chamber that the sorbent regeneration system may operate on the plurality of tiles to release captured carbon dioxide from the sorbent material and form an enriched gas.

    Systems, methods, and apparatuses for actively and continually fine-tuning convolutional neural networks to reduce annotation requirements
    9.
    发明授权
    Systems, methods, and apparatuses for actively and continually fine-tuning convolutional neural networks to reduce annotation requirements 有权

    公开(公告)号：US12216737B2

    公开(公告)日：2025-02-04

    申请号：US17698805

    申请日：2022-03-18

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

    Inventor： Zongwei Zhou ,  Jae Shin ,  Jianming Liang

    IPC: G06V10/82 ,  G06F18/21 ,  G06F18/214 ,  G06T7/00 ,  G06V10/764 ,  G16H30/40

    Abstract: Described herein are systems, methods, and apparatuses for actively and continually fine-tuning convolutional neural networks to reduce annotation requirements, in which the trained networks are then utilized in the context of medical imaging. The success of convolutional neural networks (CNNs) in computer vision is largely attributable to the availability of massive annotated datasets, such as ImageNet and Places. However, it is tedious, laborious, and time consuming to create large annotated datasets, and demands costly, specialty-oriented skills. A novel method to naturally integrate active learning and transfer learning (fine-tuning) into a single framework is presented to dramatically reduce annotation cost, starting with a pre-trained CNN to seek “worthy” samples for annotation and gradually enhances the (fine-tuned) CNN via continual fine-tuning. The described method was evaluated using three distinct medical imaging applications, demonstrating that it can reduce annotation efforts by at least half compared with random selection.

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