Adaptive Machine Learned Compressed Communication

A system comprising:

• a decoder comprising:
• one or more processors;
• and
• a memory, the memory storing computer-readable instructions that, when executed by the one or more processors of the decoder, cause the decoder to perform operations comprising:
• receiving, at the decoder and via a network, a first encoded representation of a set of input data;
• processing, by the decoder, the first encoded representation using a machine-learning decoder model to generate a first set of decoded data corresponding to the set of input data;
• processing, at the decoder, the first set of decoded data using a machine-learning encoder model to generate a second encoded representation of the set of input data, wherein the machine-learning encoder model corresponding to an encoder-side machine-learning encoder model that was used to generate the first encoded representation;
• processing, at the decoder, the second encoded representation using the machine-learning decoder model to generate a second set of decoded data;
• comparing, at the decoder, the first set of decoded data to the second set of decoded data using an objective function;
• and
• updating, at the decoder, parameters of the machine-learning decoder model and/or machine learning encoder model based on the comparison of the first set of decoded data to the second set of decoded data.

A non-transitory computer readable medium storing computer readable instructions that, when executed by a system comprising one or more processors, cause the system to perform operations comprising:

• receiving, at a decoder and via a network, a first encoded representation of a set of input data;
• processing, by the decoder, the first encoded representation using a machine-learning decoder model to generate a first set of decoded data corresponding to the set of input data;
• processing, at the decoder, the first set of decoded data using a machine-learning encoder model to generate a second encoded representation of the set of input data, wherein the machine-learning encoder model corresponding to an encoder-side machine-learning encoder model that was used to generate the first encoded representation;
• processing, at the decoder, the second encoded representation using the machine-learning decoder model to generate a second set of decoded data;
• comparing, at the decoder, the first set of decoded data to the second set of decoded data using an objective function;
• and
• updating, at the decoder, parameters of the machine-learning decoder model and/or machine learning encoder model based on the comparison of the first set of decoded data to the second set of decoded data.