Compressing images using neural networks
AssigneeDeepMind Technologies Limited
InventorsDaniel Pieter Wierstra, Karol Gregor, Frederic Olivier Besse
Life of this patent
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Abstract
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for compressing images using neural networks. One of the methods includes receiving an image; processing the image using an encoder neural network, wherein the encoder neural network is configured to receive the image and to process the image to generate an output defining values of a first number of latent variables that each represent a feature of the image; generating a compressed representation of the image using the output defining the values of the first number of latent variables; and providing the compressed representation of the image for use in generating a reconstruction of the image.
Claims20
A method comprising:
- receiving a lossy compressed representation of an image, wherein the lossy compressed representation of the image defines values of a first number of latent variables that each represent a feature of the image;
- and
- generating a reconstruction of the image from the lossy compressed representation of the image, comprising:
- selecting a value of one or more additional latent variables that are not in the first number of latent variables randomly from a prior distribution;
- and
- generating the reconstruction of the image by conditioning a generative neural network on the values of the first number of latent variables and the randomly selected values of the additional latent variables that are not in the first number of latent variables.
A system comprising one or more computers and one or more storage devices storing instructions that when executed by the one or more computers cause the one or more computers to perform operations comprising:
- receiving a lossy compressed representation of an image, wherein the lossy compressed representation of the image defines values of a first number of latent variables that each represent a feature of the image;
- and
- generating a reconstruction of the image from the lossy compressed representation of the image, comprising:
- selecting a value of one or more additional latent variables that are not in the first number of latent variables randomly from a prior distribution;
- and
- generating the reconstruction of the image by conditioning a generative neural network on the values of the first number of latent variables and the randomly selected values of the additional latent variables that are not in the first number of latent variables.
One or more non-transitory computer-readable storage media storing instructions that when executed by one or more computers cause the one or more computers to perform operations comprising:
- receiving a lossy compressed representation of an image, wherein the lossy compressed representation of the image defines values of a first number of latent variables that each represent a feature of the image;
- and
- generating a reconstruction of the image from the lossy compressed representation of the image, comprising:
- selecting a value of one or more additional latent variables that are not in the first number of latent variables randomly from a prior distribution;
- and
- generating the reconstruction of the image by conditioning a generative neural network on the values of the first number of latent variables and the randomly selected values of the additional latent variables that are not in the first number of latent variables.
Dependencies are inferred from the claim text.