Learning apparatus and method, and trained model
AssigneeKabushiki Kaisha Toshiba
InventorShintaro HARADA
Life of this patent
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Abstract
According to one embodiment, a learning apparatus includes a processor. The processor acquires a first token sequence in which input data is divided into tokens. The processor generates a second token sequence in which noise is added to the first token sequence. The processor calculates a first feature from the first token sequence and a second feature from the second token sequence using a model for extracting features. The processor calculates a transport cost required for approximating the second feature to the first feature. The processor updates the model based on the transport cost.
Claims13
A learning apparatus comprising a processor configured to:
- acquire a first token sequence in which input data is divided into tokens;
- generate a second token sequence in which noise is added to the first token sequence;
- calculate a first feature from the first token sequence and a second feature from the second token sequence using a model for extracting features;
- calculate a transport cost required for approximating the second feature to the first feature;
- and
- update the model based on the transport cost.
A learning method comprising:
- acquiring a first token sequence in which input data is divided into tokens;
- generating a second token sequence in which noise is added to the first token sequence;
- calculating a first feature from the first token sequence and a second feature from the second token sequence by using a model for extracting features;
- calculating a transport cost required for approximating the second feature to the first feature;
- and
- updating the model based on the transport cost.
A trained model comprising a network layer that processes input data and infers output data, the trained model being trained by:
- a generation step of generating a second token sequence in which noise is added to a first token sequence;
- a feature calculation step of calculating a first feature from the first token sequence and a second feature from the second token sequence by using a model for extracting features;
- a cost calculation step of calculating a transport cost required for approximating the second feature to the first feature;
- and
- an update step of updating the model based on the transport cost,
- the trained model causing a computer to input the input data to the network layer to which an updated parameter is assigned and to infer the output data.
Dependencies are inferred from the claim text.