Object recognition with reduced neural network weight precision

A method that comprises performing the following using a client device:

• receiving a trained neural network from a server system, wherein the server system has trained and configured a server-based neural network to be used as the trained neural network for the client device;
• capturing an input image;
• processing the input image using the trained neural network;
• generating a feature map using the trained neural network, the feature map comprising a plurality of weight values derived from an input image;
• performing a unitary quantizing operation or a supervised iterative quantization operation on the feature map using the trained neural network to reduce a number of bits of each weight of the plurality of weight values from a first predetermined number to a second predetermined number that is less than the first predetermined number without changing a dimension of the feature map;
• and
• recognizing an object in the input image based on a result of the processing.

A non-transitory computer-readable medium storing program code, which, when executed by a processor, cause the processor to perform the following:

• receive a trained neural network from a server system, wherein the server system has trained and configured a server-based neural network to be used as the trained neural network;
• capture an input image;
• generating a feature map using the trained neural network, the feature map comprising a plurality of weight values derived from an input image;
• performing a unitary quantizing operation or supervised iterative quantization operation on the feature map using the trained neural network to reduce a number of bits of each weight of the plurality of weight values from a first predetermined number to a second predetermined number that is less than the first predetermined number without changing a dimension of the feature map;
• and
• process the input image using the trained neural network to recognize an object in the input image.

A client device configured with a trained neural network, the client device comprising:

• a processor, a memory, a user interface, a communications interface, a power supply and an input device;
• the memory comprising the trained neural network received from a server system, wherein the server system has trained and configured a server-based neural network to be used as the trained neural network for the client device;
• wherein:
• the trained neural network is configured to generate a feature map, the feature map comprising a plurality of first weight values derived from an input image;
• the trained neural network is configured to convert the first weights of the feature map into second weights by a unitary or a supervised iterative quantizing operation;
• and
• the second weights are encoded using a number of bits lower than that used to encode the first weights without changing a dimension of the feature map.