Image hash codes generated by a neural network

A system comprising:

• a processor;
• and
• a memory coupled to the processor, the memory storing instructions that, when executed by the processor, cause the processor to perform operations, the operations including:
• during a training stage of a neural network, concurrently performing a first task and a second task at the neural network on data associated with multiple subsets of images of a plurality of images of a training image set, wherein the first task includes adjusting a first set of parameters based on a classification loss function to generate an adjusted first set of parameters, wherein the second task includes adjusting a second set of parameters based on a hashing loss function, and wherein the concurrently performing the first task and the second task reduces parameter instability associated with the hashing loss function;
• and
• after the training stage, generating a deep hash data structure based on the first set of parameters and the second set of parameters determined from the training image set.

A device comprising:

• a processor;
• a memory coupled to the processor, the memory storing instructions that, when executed by the processor, cause the processor to perform operations, the operations including:
• receiving a plurality of deep hash parameters of a deep hash data structure;
• and
• utilizing an image hashing application to generate, based on the plurality of deep hash parameters, an image signature for an image file, the image signature having a first number of bits that is less than a second number of bits associated with the image file, wherein the plurality of deep hash parameters is obtained, at a neural network during a training stage, by iteratively adjusting parameters of the neural network based on concurrent application of multiple loss functions to data associated with a plurality of images of a training image set, the multiple loss functions including a classification loss function and a hashing loss function, and the concurrent application of the multiple loss functions causing a reduction of parameter instability associated with the hashing loss function.