Qualcomm Incorporated

Johann Hinrich Brehmer

Taco Sebastiaan Cohen

Ties Jehan Van Rozendaal

Yunfan Zhang

Markus Nagel

Techniques are described for compressing data using machine learning systems. An example process can include receiving input data for compression by a neural network compression system. The process can include determining, based on the input data, a set of updated model parameters for the neural network compression system, wherein the set of updated model parameters is selected from a subspace of model parameters. The process can include generating at least one bitstream including a compressed version of the input data and a compressed version of one or more subspace coordinates that correspond to the set of updated model parameters. The process can include outputting the at least one bitstream for transmission to a receiver.

Instance-adaptive image and video compression in a network parameter subspace using machine learning systems

Techniques are described for compressing and decompressing data using machine learning systems. An example process can include receiving a plurality of images for compression by a neural network compression system. The process can include determining, based on a first image from the plurality of images, a first plurality of weight values associated with a first model of the neural network compression system. The process can include generating a first bitstream comprising a compressed version of the first plurality of weight values. The process can include outputting the first bitstream for transmission to a receiver.

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Amsterdam, Netherlands

Johann Hinrich Brehmer