Systems and methods for stochastic generative hashing

A computer-implemented method for learning to produce compressed binary hashes, the method comprising:

• obtaining, by one or more computing devices, an input;
• inputting, by the one or more computing devices, the input into a machine-learned hashing model;
• receiving, by the one or more computing devices, a binary hash for the input as an output of the machine-learned hashing model, wherein the binary hash comprises a compressed representation of the input;
• inputting, by the one or more computing devices, the binary hash into a machine-learned generative model;
• receiving, by the one or more computing devices, a reconstruction of the input as an output of the machine-learned generative model;
• evaluating, by the one or more computing devices, an objective function based on at least one of the binary hash and the reconstruction of the input to obtain an objective value, wherein the objective function comprises a description length term that penalizes a description length of the binary hash;
• and
• adjusting, by the one or more computing devices, one or more parameters of the machine-learned hashing model to improve the objective value.

A computer system for learning to produce compressed binary hashes comprising:

• a machine-learned hashing model configured to receive an input and, in response, output a binary hash for the input, wherein the binary hash comprises a compressed representation of the input, wherein the binary hash further comprises a binary value for each of a plurality of binary variables, wherein the machine-learned hashing model has been trained based at least in part on an objective function that evaluates the binary hash or a reconstruction of the input provided by a machine-learned generative model in response to receipt of the binary hash, wherein the objective function comprises a description length term that penalizes a description length of the binary hash, and wherein the objective function further comprises a plurality of stochastic neurons that reparameterize the plurality of binary variables;
• one or more processors;
• a database that stores item binary hashes for a plurality of items;
• one or more non-transitory computer-readable media that collectively store instructions that, when executed by the one or more processors, cause the computer system to perform operations, the operations comprising:
• obtaining a query;
• inputting the query into the machine-learned hashing model;
• receiving a query binary hash as an output of the machine-learned hashing model;
• and
• identifying one or more similar items from the plurality of items based at least on part on a comparison of the query binary hash to one or more of the item binary hashes.