Data compression using conditional entropy models

A method implemented by a data processing apparatus, the method comprising:

• obtaining entropy encoded representations of:
• (i) a latent representation of a set of data, and (ii) a latent representation of an entropy model used to entropy encode the latent representation of the data,
• wherein the latent representation of the entropy model was determined by processing the latent representation of the data using a hyper-encoder neural network, and
• wherein the entropy model is defined by one or more probability distribution parameters characterizing one or more code symbol probability distributions;
• entropy decoding the latent representation of the data, comprising:
• determining the probability distribution parameters defining the entropy model using the latent representation of the entropy model;
• and
• entropy decoding the latent representation of the data using the entropy model;
• and
• determining a reconstruction of the data from the latent representation of the data, comprising processing the latent representation of the data using a using a decoder neural network.

One or more non-transitory computer storage media storing instructions that when executed by one or more computers cause the one or more computers to perform operations comprising:

• processing data using an encoder neural network to generate a latent representation of the data;
• processing the latent representation of the data using a hyper-encoder neural network to generate a latent representation of an entropy model, wherein the entropy model is defined by one or more probability distribution parameters characterizing one or more code symbol probability distributions;
• generating an entropy encoded representation of the latent representation of the entropy model;
• generating an entropy encoded representation of the latent representation of the data using the latent representation of the entropy model, comprising:
• determining the probability distribution parameters defining the entropy model using the latent representation of the entropy model;
• and
• generating the entropy encoded representation of the latent representation of the data using the entropy model;
• and
• determining a compressed representation of the data from the entropy encoded representations of:
• (i) the latent representation of the data and (ii) the latent representation of the entropy model used to entropy encode the latent representation of the data.

A system comprising:

• one or more computers;
• and
• one or more storage devices communicatively coupled to the one or more computers, wherein the one or more storage devices store instructions that, when executed by the one or more computers, cause the one or more computers to perform operations comprising:
• processing data using an encoder neural network to generate a latent representation of the data;
• processing the latent representation of the data using a hyper-encoder neural network to generate a latent representation of an entropy model, wherein the entropy model is defined by one or more probability distribution parameters characterizing one or more code symbol probability distributions;
• generating an entropy encoded representation of the latent representation of the entropy model;
• generating an entropy encoded representation of the latent representation of the data using the latent representation of the entropy model, comprising:
• determining the probability distribution parameters defining the entropy model using the latent representation of the entropy model;
• and
• generating the entropy encoded representation of the latent representation of the data using the entropy model;
• and
• determining a compressed representation of the data from the entropy encoded representations of:
• (i) the latent representation of the data and (ii) the latent representation of the entropy model used to entropy encode the latent representation of the data.