GENERATING A DIGITAL IMAGE USING A GENERATIVE ADVERSARIAL NETWORK

    公开(公告)号:US20190286950A1

    公开(公告)日:2019-09-19

    申请号:US15923347

    申请日:2018-03-16

    Applicant: eBay Inc.

    Abstract: Various embodiments described herein utilize multiple levels of generative adversarial networks (GANs) to facilitate generation of digital images based on user-provided images. Some embodiments comprise a first generative adversarial network (GAN) and a second GAN coupled to the first GAN, where the first GAN includes an image generator and at least two discriminators, and the second GAN includes an image generator and at least one discriminator. According to some embodiments, the (first) image generator of the first GAN is trained by processing a user-provided image using the first GAN. For some embodiments, the user-provided image and the first generated image, generated by processing the user-provided image using the first GAN, are combined to produce a combined image. For some embodiments, the (second) image generator of the second GAN is trained by processing the combined image using the second GAN.

    IMAGE EVALUATION
    182.
    发明申请
    IMAGE EVALUATION 审中-公开

    公开(公告)号:US20190122083A1

    公开(公告)日:2019-04-25

    申请号:US16225338

    申请日:2018-12-19

    Applicant: eBay Inc.

    Abstract: A machine may he configured to perform image evaluation of images depicting items for sale and to provide recommendations for improving the images depicting the items to increase the sales of the items depicted in the images. For example, the machine accesses a result of a user behavior analysis. The machine receives an image of an item from a user device. The machine performs an image evaluation of the received image based on an analysis of the received image and the result of the user behavior analysis. The performing of the image evaluation may include determining a likelihood of a user engaging in a desired user behavior in relation to the received image. Then, the machine generates, based on the evaluation of the received image, an output that references the received image and indicates the likelihood of a user engaging in the desired behavior.

    Estimating depth from a single image

    公开(公告)号:US10255686B2

    公开(公告)日:2019-04-09

    申请号:US15408648

    申请日:2017-01-18

    Applicant: eBay Inc.

    Abstract: During a training phase, a machine accesses reference images with corresponding depth information. The machine calculates visual descriptors and corresponding depth descriptors from this information. The machine then generates a mapping that correlates these visual descriptors with their corresponding depth descriptors. After the training phase, the machine may perform depth estimation based on a single query image devoid of depth information. The machine may calculate one or more visual descriptors from the single query image and obtain a corresponding depth descriptor for each visual descriptor from the generated mapping. Based on obtained depth descriptors, the machine creates depth information that corresponds to the submitted single query image.

    Camera Platform incorporating Schedule and Stature

    公开(公告)号:US20190080172A1

    公开(公告)日:2019-03-14

    申请号:US15859056

    申请日:2017-12-29

    Applicant: eBay Inc.

    Abstract: Camera platform techniques are described. In an implementation, a plurality of digital images and data describing times, at which, the plurality of digital images are captured is received by a computing device. Objects of clothing are recognized from the digital images by the computing device using object recognition as part of machine learning. A user schedule is also received by the computing device that describes user appointments and times, at which, the appointments are scheduled. A user profile is generated by the computing device by training a model using machine learning based on the recognized objects of clothing, times at which corresponding digital images are captured, and the user schedule. From the user profile, a recommendation is generated by processing a subsequent user schedule using the model as part of machine learning by the computing device.

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