MOBILE-BASED CARTOGRAPHIC CONTROL OF DISPLAY CONTENT

    公开(公告)号:US20190130616A1

    公开(公告)日:2019-05-02

    申请号:US15797859

    申请日:2017-10-30

    Applicant: Snap Inc.

    Abstract: A content display system can control which content and how the content is displayed based on viewing parameters, such as a map zoom level, and physical distance parameters, e.g., a geo-fence distance and an icon visibility distance. Different combinations of input (e.g., zoom level and physical distances) yield a myriad of pre-set content displays on the client device, thereby allowing a creator of an icon to finely tune how content displayed otherwise accessed.

    REDUNDANT TRACKING SYSTEM
    56.
    发明申请

    公开(公告)号:US20180114364A1

    公开(公告)日:2018-04-26

    申请号:US15792347

    申请日:2017-10-24

    Applicant: Snap Inc

    Abstract: A redundant tracking system comprising multiple redundant tracking sub-systems, enabling seamless transitions between such tracking sub-systems, provides a solution to this problem by merging multiple tracking approaches into a single tracking system. This system is able to combine tracking objects with six degrees of freedom (6DoF) and 3DoF through combining and transitioning between multiple tracking systems based on the availability of tracking indicia tracked by the tracking systems. Thus, as the indicia tracked by any one tracking system becomes unavailable, the redundant tracking system seamlessly switches between tracking in 6DoF and 3DoF thereby providing the user with an uninterrupted experience.

    STEP DISTILLATION FOR LATENT DIFFUSION MODELS

    公开(公告)号:US20240394843A1

    公开(公告)日:2024-11-28

    申请号:US18434411

    申请日:2024-02-06

    Applicant: Snap Inc.

    Abstract: Described is a system for improving machine learning models by accessing a first latent diffusion machine learning model, the first latent diffusion machine learning model trained to perform a first number of denoising steps, accessing a second latent diffusion machine learning model that was derived from the first latent diffusion machine learning model, the second latent diffusion machine learning model trained to perform a second number of denoising steps, generating noise data, processing the noise data via the first latent diffusion machine learning model to generate one or more first images, processing the noise data via the second latent diffusion machine learning model to generate one or more second images, and modify a parameter of the second latent diffusion machine learning model based on a comparison of the one or more first images with the one or more second images.

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