Training apparatus, classification apparatus, training method, classification method, and program

A classification apparatus comprising at least one processor, the at least one processor carrying out:

• a feature extraction process for extracting, by feature extractors, feature values from input image data;
• and
• a class prediction process for predicting, by class predictors, class prediction values from the feature values,
• wherein at least one of the feature extractors and the class predictors has been trained with reference to:
• an extraction process for extracting, by one or more feature extractors, source domain feature values from input source domain image data and for extracting target domain feature values from input target domain image data
• an angle conversion process for generating, by one or more angle converters:
• converted source domain feature values by converting the source domain feature values as if the converted source domain feature values are extracted from source domain image data having different angles from the input source domain image data, and
• converted target domain feature values by converting the target domain feature values as if the converted target domain feature values are extracted from target domain image data having different angles from the input target domain image data;
• a second class prediction process for predicting, by one or more class predictors:
• source domain class prediction values from the source domain feature values and the converted source domain feature values, and
• target domain class prediction values from the target domain feature values and the converted target domain feature values;
• and
• an updating process for updating at least one of (i) the one or more feature extractors, (ii) the one or more angle converters, and (iii) the one or more class predictors with reference to:
• a source domain classification loss computed with reference to the source domain class prediction values,
• a target domain classification loss computed with reference to the target domain class prediction values,
• a conversion loss computed with reference to the source domain feature values, the converted source domain feature values, the target domain feature values and the converted target domain feature values, and
• a grouping loss computed with reference to the source domain feature values, the converted source domain feature values, the target domain feature values, and the converted target domain feature values.

A training method comprising:

• extracting, by one or more feature extractors:
• source domain feature values from input source domain image data, and
• target domain feature values from input target domain image data;
• generating, by one or more angle converters,
• converted source domain feature values by converting the source domain feature values as if the converted source domain feature values are extracted from source domain image data having different angles from the input source domain image data, and
• converted target domain feature values by converting the target domain feature values as if the converted target domain feature values are extracted from target domain image data having different angles from the input target domain image data;
• predicting, by one or more class predictors:
• source domain class prediction values from the source domain feature values and the converted source domain feature values, and
• target domain class prediction values from the target domain feature values, and the converted target domain feature values;
• and
• updating at least one of (i) the one or more feature extractors, (ii) the one or more angle converters, and (iii) the one or more class predictors with reference to
• a source domain classification loss computed with reference to the source domain class prediction values,
• a target domain classification loss computed with reference to the target domain class prediction values,
• a conversion loss computed with reference to the source domain feature values, the converted source domain feature values, the target domain feature values and the converted target domain feature values, and
• a grouping loss computed with reference to the source domain feature values, the converted source domain feature values, the target domain feature values, and the converted target domain feature values.