Information processing apparatus, method and non-transitory computer readable medium
AssigneeKabushiki Kaisha Toshiba
InventorsYuichi KATO, Kentaro TAKAGI, Kouta NAKATA
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Abstract
According to one embodiment, an information processing apparatus includes a processor. The processor acquires training data that is used for training of a first feature extractor and a second feature extractor. The processor determines a model size of the second feature extractor. The processor extracts a first feature by inputting the training data to the first feature extractor. The processor extracts a second feature by inputting the first feature to the second feature extractor. The processor trains the first feature extractor in such a manner as to make the first feature closer to the second feature.
Claims9
An information processing apparatus comprising a processor configured to:
- acquire training data that is used for training of a first feature extractor and a second feature extractor;
- determine a model size of the second feature extractor;
- extract a first feature by inputting the training data to the first feature extractor;
- extract a second feature by inputting the first feature to the second feature extractor;
- and
- train the first feature extractor in such a manner as to make the first feature closer to the second feature.
An information processing method comprising:
- acquiring training data that is used for training of a first feature extractor and a second feature extractor;
- determining a model size of the second feature extractor;
- extracting a first feature by inputting the training data to the first feature extractor;
- extracting a second feature by inputting the first feature to the second feature extractor;
- and
- training the first feature extractor in such a manner as to make the first feature closer to the second feature.
A non-transitory computer readable medium including computer executable instructions, wherein the instructions, when executed by a processor, cause the processor to perform a method comprising:
- acquiring training data that is used for training of a first feature extractor and a second feature extractor;
- determining a model size of the second feature extractor;
- extracting a first feature by inputting the training data to the first feature extractor;
- extracting a second feature by inputting the first feature to the second feature extractor;
- and
- training the first feature extractor in such a manner as to make the first feature closer to the second feature.
Dependencies are inferred from the claim text.