System and method for learning from partial compressed representation
OwnerMinghai QIN, Zhenzhen WANG +1inventors
InventorsMinghai QIN, Zhenzhen WANG, Yen-Kuang CHEN
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Abstract
The present disclosure relates to a system and method for machine learning from partial compressed representation. In some embodiments, an exemplary machine learning system includes: a compressor having circuitry configured to use a compression neural network to compress an image into a compressed representation, the compressed representation comprising a sequence of compressed channels; a selector having circuitry configured to select a part of the compressed channels from the compressed representation; and a learning module having circuitry configured to perform a learning task on the selected compressed channels.
Claims20
A machine learning system, comprising:
- a compressor having circuitry configured to use a compression neural network to compress an image into a compressed representation, the compressed representation comprising a sequence of compressed channels;
- a selector having circuitry configured to select a part of the compressed channels from the compressed representation;
- and
- a learning module having circuitry configured to perform a learning task on the selected compressed channels.
A method for machine learning, comprising:
- compressing an image with a compression neural network to generate a compressed representation comprising a sequence of compressed channels;
- selecting a part of the compressed channels from the compressed representation;
- and
- performing a learning task on the selected compressed channels.
An apparatus for machine learning, comprising:
- at least one memory for storing instructions;
- and
- at least one processor configured to execute the instructions to cause the apparatus to perform:
- compressing an image with a neural network to generate a compressed representation comprising a plurality of compressed channels;
- selecting a part of the compressed channels from the compressed representation;
- and
- performing a learning task on the selected compressed channels.
A non-transitory computer readable storage medium storing a set of instructions that are executable by one or more processing devices to cause a computer to perform:
- compressing an image with a neural network to generate a compressed representation comprising a plurality of compressed channels;
- selecting a part of the compressed channels from the compressed representation;
- and
- performing a learning task on the selected compressed channels.
Dependencies are inferred from the claim text.