Similarity Search Using Polysemous Codes

In one embodiment, a method includes receiving a query, wherein the query is represented by an n-dimensional vector in an n-dimensional vector space; quantizing the vector representing the query using a quantizer, wherein the quantized vector corresponds to a polysemous code, and wherein the quantizer has been trained by machine learning to determine polysemous codes such that the Hamming distance approximates the inter-centroid distance using an objective function; calculating, for each of a plurality of content objects, a Hamming distance between the polysemous code corresponding to the vector representing the query and a polysemous code corresponding to a quantized vector representing the content object; and determining that a content object of the plurality of content objects is an approximate nearest neighbor to the query based on determining that the calculated Hamming distance is less than a threshold amount.