COMPRESSING IMAGE-TO-IMAGE MODELS

    公开(公告)号:US20220207329A1

    公开(公告)日:2022-06-30

    申请号:US17558327

    申请日:2021-12-21

    Applicant: Snap Inc.

    Abstract: Systems and methods herein describe an image compression system. The image compression system generates a first generative adversarial network (GAN), identifies a threshold, based on the threshold, generates a second GAN by pruning channels of the first GAN, trains the second GAN using similarity-based knowledge distillation from the first GAN, and stores the trained second GAN.

    AUTOMATIC QUANTIZATION OF A FLOATING POINT MODEL

    公开(公告)号:US20240053959A1

    公开(公告)日:2024-02-15

    申请号:US18331660

    申请日:2023-06-08

    Applicant: Snap Inc.

    CPC classification number: G06F7/483

    Abstract: Aspects of the present disclosure involve a system comprising a computer-readable storage medium storing a program and method for automatic quantization of a floating point model. The program and method provide for providing a floating point model to an automatic quantization library, the floating point model being configured to represent a neural network, and the automatic quantization library being configured to generate a first quantized model based on the floating point model; providing a function to the automatic quantization library, the function being configured to run a forward pass on a given dataset for the floating point model; causing the automatic quantization library to generate the first quantized model based on the floating point model; causing the automatic quantization library to calibrate the first quantized model by running the first quantized model on the function; and converting the calibrated first quantized model to a second quantized model.

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