Multi-domain convolutional neural network

A system, comprising:

• one or more sensors to capture a visual representation of an environment;
• and
• one or more processing devices to:
• obtain visual data associated with the visual representation captured by the one or more sensors, wherein the visual data comprises uncompressed visual data or compressed visual data;
• process the visual data using a convolutional neural network (CNN), wherein the CNN comprises a plurality of layers, the plurality of layers comprises a plurality of filters, and the plurality of filters comprises one or more pixel-domain filters and one or more compressed-domain filters, wherein:
• the one or more pixel-domain filters are for performing processing associated with uncompressed data;
• the one or more compressed-domain filters are for performing processing associated with compressed data;
• and
• the CNN is trained to rely on processing associated with the one or more pixel-domain filters when the visual data is uncompressed, and the CNN is trained to rely on processing associated with the one or more compressed-domain filters when the visual data is compressed;
• and
• classify the visual data based on an output of the CNN.

At least one non-transitory machine accessible storage medium having instructions stored thereon, wherein the instructions, when executed on a machine, cause the machine to:

• obtain visual data associated with a visual representation captured by one or more sensors, wherein the visual data comprises uncompressed visual data or compressed visual data;
• process the visual data using a convolutional neural network (CNN), wherein the CNN comprises a plurality of layers, the plurality of layers comprises a plurality of filters, and the plurality of filters comprises one or more pixel-domain filters and one or more compressed-domain filters, wherein:
• the one or more pixel-domain filters are for performing processing associated with uncompressed data;
• the one or more compressed-domain filters are for performing processing associated with compressed data;
• and
• the CNN is trained to rely on processing associated with the one or more pixel-domain filters when the visual data is uncompressed, and the CNN is trained to rely on processing associated with the one or more compressed-domain filters when the visual data is compressed;
• and
• classify the visual data based on an output of the CNN.

A method, comprising:

• obtaining visual data associated with a visual representation captured by one or more sensors, wherein the visual data comprises uncompressed visual data or compressed visual data;
• processing the visual data using a convolutional neural network (CNN), wherein the CNN comprises a plurality of layers, the plurality of layers comprises a plurality of filters, and the plurality of filters comprises one or more pixel-domain filters and one or more compressed-domain filters, wherein:
• the one or more pixel-domain filters are for performing processing associated with uncompressed data;
• the one or more compressed-domain filters are for performing processing associated with compressed data;
• and
• the CNN is trained to rely on processing associated with the one or more pixel-domain filters when the visual data is uncompressed, and the CNN is trained to rely on processing associated with the one or more compressed-domain filters when the visual data is compressed;
• and
• classifying the visual data based on an output of the CNN.