A transfer learning apparatus includes a transfer target data evaluator and an output layer adjuster. The transfer target data evaluator inputs a plurality of labeled transfer target data items each assigned a label of a corresponding evaluation item from among one or more evaluation items to a neural network apparatus having been trained by using a plurality of labeled transfer source data items and including in an output layer output units, the number of which is larger than or equal to the number of evaluation items, and obtains evaluation values output from the respective output units. The output layer adjuster preferentially assigns, to each of the one or more evaluation items, an output unit from which the evaluation value having the smallest difference from the label of the evaluation item is obtained with a higher frequency, as an output unit that outputs the evaluation value of the evaluation item.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A transfer learning apparatus comprising: a transfer target data evaluator that inputs a plurality of labeled transfer target data items to a neural network apparatus, each of the plurality of labeled transfer target data items being assigned a label of a corresponding evaluation item from among one or more evaluation items, the neural network apparatus having been trained by using a plurality of labeled transfer source data items and including in an output layer one or more output units, the number of which is larger than or equal to the number of evaluation items, and that obtains evaluation values output from the respective output units; and an output layer adjuster that preferentially assigns, to each of the one or more evaluation items, an output unit from which the evaluation value having the smallest difference from the label of the evaluation item is obtained with a higher frequency among the output units, as an output unit that outputs the evaluation value of the evaluation item. 2. The transfer learning apparatus according to claim 1, wherein the output layer adjuster computes, for each of the one or more evaluation items, a frequency distribution of the output units from which the evaluation value having the smallest difference from the label of the evaluation item is obtained for the plurality of labeled transfer target data items, preferentially selects, from among combinations for associating each of the one or more evaluation items with a different one of the output units, a combination for which a sum of the frequencies of the associated output units is larger, and assigns, to each of the one or more evaluation items, the output unit associated with the evaluation item by the selected combination as an output unit for the evaluation item. 3. The transfer learning apparatus according to claim 1, further comprising: a weight adjuster that updates, by using a weight value that has been learned with the plurality of labeled transfer source data items as an initial value, the weight value in the neural network apparatus through supervised learning using the plurality of labeled transfer target data items. 4. The transfer learning apparatus according to claim 3, wherein the weight adjuster sets weight values of all units included in the neural network apparatus as targets to be updated. 5. The transfer learning apparatus according to claim 3, wherein each of the plurality of labeled transfer source data items is assigned a label corresponding to one of the output units, and wherein the weight adjuster further updates the weight value in the neural network apparatus through supervised learning using labels corresponding to the output units assigned by the output layer adjuster from among the labels assigned to the plurality of labeled transfer source data items. 6. The transfer learning apparatus according to claim 3, wherein the weight adjuster performs unsupervised learning in each of one or more hidden layers included in the neural network apparatus prior to the supervised learning. 7. The transfer learning apparatus according to claim 1, wherein the neural network apparatus includes in the output layer the output units, the number of which is larger than the number of evaluation items, and wherein the transfer learning apparatus further comprises: a learning deficiency detector that inputs one or more unlabeled transfer target data items to the neural network apparatus, obtains an evaluation value output from an output unit that is not assigned by the output layer adjuster among the output units, and detects deficiency in learning if the evaluation value deviates from a reference range. 8. The transfer learning apparatus according to claim 7, further comprising: a correct solution obtainer that accepts, in a case where deficiency in learning is detected for the unlabeled transfer target data item, a label corresponding to the unlabeled transfer target data item from a user, wherein the weight adjuster updates the weight value in the neural network apparatus through supervised learning using the label accepted from the user and the unlabeled transfer target data item. 9. The transfer learning apparatus according to claim 8, wherein the correct solution obtainer accepts, from the user, a label that is common to a plurality of unlabeled transfer target data items for which evaluation values output from a single output unit that is not assigned by the output layer adjuster deviate from the reference range, and wherein the weight adjuster updates the weight value in the neural network apparatus through supervised learning using the label accepted from the user and the plurality of unlabeled transfer target data items. 10. The transfer learning apparatus according to claim 8, wherein each of the plurality of labeled transfer target data items is further assigned an associative data item, wherein the correct solution obtainer accepts, from the user, an associative data item for the unlabeled transfer target data item, and wherein the weight adjuster performs the supervised learning by using a label of a labeled transfer target data item that is assigned the associative data item accepted from the user and the unlabeled transfer target data item. 11. A transfer learning system comprising: the transfer learning apparatus according to claim 1; and the neural network apparatus, wherein the transfer learning apparatus performs transfer learning in the neural network apparatus. 12. A transfer learning method comprising: inputting a plurality of labeled transfer target data items to a neural network apparatus, each of the plurality of labeled transfer target data items being assigned a label of a corresponding evaluation item from among one or more evaluation items, the neural network apparatus having been trained by using a plurality of labeled transfer source data items having evaluation items different from the plurality of labeled transfer target data items and including in an output layer one or more output units, the number of which is larger than or equal to the number of evaluation items, and obtaining evaluation values output from the respective output units; and preferentially assigning, to each of the one or more evaluation items, an output unit from which the evaluation value having the smallest difference from the label of the evaluation item is obtained with a higher frequency among the output units, as an output unit that outputs the evaluation value of the evaluation item. 13. A non-transitory computer-readable recording medium storing thereon a program for causing a device including a processor to execute a transfer learning process, the transfer learning process comprising: inputting a plurality of labeled transfer target data items to a neural network apparatus, each of the plurality of labeled transfer target data items being assigned a label of a corresponding evaluation item from among one or more evaluation items, the neural network apparatus having been trained by using a plurality of labeled transfer source data items having evaluation items different from the plurality of labeled transfer target data items and including in an output layer one or more output units, the number of which is larger than or equal to the number of evaluation items, and obtaining evaluation values output from the respective output units; and preferentially assigning, to each of the one or more evaluation items, an output unit from which the evaluation value having the smallest difference from the label of the evaluation item is obtained with a higher frequency among the output units, as an output unit that outputs the evaluation value of the evaluation item. 14. A transfer learning method comprising: performing a first process by using a neural network including a plurality of units; performing a second process by using the neural network; and performing a third process; wherein a plurality of weight values for the plurality of units are determined by using a plurality of first datasets prior to the first process and the second process, wherein the plurality of units include a plurality of output layer units included in an output layer of the neural network, and each of the plurality of first datasets includes a plurality of pixel values of an image not related to a first subject matter, wherein the first process includes a fourth process and a fifth process, wherein the fourth process includes performing a sixth process n times while incrementing k from 1 to n, where n denotes a total number of a plurality of second datasets and k is a natural number, the sixth process including inputting a k-th dataset of the plurality of second datasets to the neural network and obtaining k-th information indicating an output layer unit that outputs a first valid value from among the plurality of output layer units, wherein the fifth process includes determining, for each of the plurality of output layer units, a probability of outputting the first valid value on the basis of a result of the fourth process, wherein the second process includes a seventh process and an eighth process, wherein the seventh process includes performing a ninth process m times while incrementing j from 1 to m, where m denotes a total number of a plurality of third datasets and j denotes a natural number, the ninth process including inputting a j-th dataset of the plurality of third datasets to the neural network and obtaining j-th information indicating an output layer unit that outputs a second valid value from among the plurality of output layer units, wherein the eighth process includes determining, for each of the plurality of output layer units, a probability of outputting the second valid value on the basis of a result of the seventh process, wherein each of the plurality of second datasets and the plurality of third datasets includes a plurality of pixel values of an image related to the first subject matter, each of the plurality of second datasets has attribute information indicating a first data type, each of the plurality of third datasets has attribute information indicating a second data type different from the first data type, and the number of first datasets is larger than (n+m), and wherein the third process is a process of selecting two output layer units from among the plurality of output layer units on the basis of a result of the first process and a result of the second process. 15. The transfer learning method according to claim 14, wherein the image not related to the first subject matter is a non-organ image and the image related to the first subject matter is an organ image, and wherein the attribute information indicating the first data type is the attribute information indicating a lesion and the attribute information indicating the second data type is the attribute information indicating a non-lesion.