Patent Publication Number: US-2022222552-A1

Title: Data-creation assistance apparatus and data-creation assistance method

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application claims priority pursuant to Japanese patent application No. 2021-003982, filed on Jan. 14, 2021, the entire disclosure of which is incorporated herein by reference. 
     BACKGROUND OF THE INVENTION 
     1. Field of the Invention 
     The present invention relates to a data-creation assistance apparatus and a data-creation assistance method. 
     2. Description of the Related Art 
     Since Internet of Things (IoT) technique is widely used and a large amount of observation data can be acquired, a machine learning technique that efficiently specifies some significant events and rules from such data continues to evolve. 
     In such machine learning, that is, supervised machine learning, by giving a training data group to which a label is attached to a learning model, learning that recognizes features of an event indicated by the training data with high accuracy and derives a correct inference result, that is, a value of the label proceeds. 
     Superficially, there are cases where emphasis is only placed on a fact that the inference result acquired by such machine learning is a ground truth, but the “ground truth” may also include, for example, a result of a “ground truth” even for data (having a different distribution) that is not included in the training data and that would otherwise be difficult to derive the ground truth. 
     Of course, it is an advantage of machine learning to acquire features outside the training data as a generalization performance, but it is not desirable to have a situation in which the correct answer is made even for those that have obviously not been learned. 
     As a related-art technique related to such supervised machine learning, for example, a technique (see JP-A-2018-200677) for adjusting parameters of a deep learning network so as to optimize an accuracy and an uncertainty of a prediction at the same time has been proposed. 
     This technique is a management method that initializes deep learning architecture parameters of a predefined base architecture, performs a model training based on the deep learning architecture parameters to generate a trained model, iteratively applies Monte Carlo (MC) dropout to the generated trained model to acquire the accuracy and the uncertainty of the prediction, configures a fitness function to evaluate the accuracy and the uncertainty of the prediction of the trained model, in which when the fitness function indicates that the trained model is not optimized, the deep learning architecture parameters is updated to iterate over the generation of the trained model and the evaluation of the fitness function, and when the fitness function indicates that the trained model has been optimized, the trained model for the prediction is provided. 
     A point in recognizing a problem related to the “correct answer” described above is a difference between the accuracy and the uncertainty. Originally, a case where the correct answer is given regardless of an intention of a developer, even though learning has not been performed, is a situation in which the accuracy is high but the uncertainty is also high, which is undesirable. 
     Therefore, it is necessary to perform verification in terms of whether the learned machine learning model outputs an inference result according to a specification (training data) as test data, whether the inference result can be trusted no matter how far the data is from the specification, and whether the learned machine learning model not only simply outputs the ground truth (with high accuracy) but also whether learned machine learning model makes the correct answer to those that should be made a correct answer and does not make a correct answer to those that should not be made a correct answer. 
     According to the related-art technique, a learning method for optimizing the uncertainty and the accuracy at the same time is presented, but no mechanism is disclosed to verify what kind of data indicates the uncertainty or certainty (and how the data indicates the uncertainty or certainty in the inference result). As a result, it is not possible to efficiently verify and improve a robustness of the learning model. 
     SUMMARY 
     Therefore, an object of the invention is to provide a technique for efficiently verifying and improving the robustness of the learning model for supervised machine learning. 
     In order to solve the above problem, a data-creation assistance apparatus of the invention includes: a storage device configured to store neural network model used for supervised machine learning and test data attached with a label of ground truth; and a computing device configured to execute a process of specifying an uncertainty of an inference result from the neural network model by inputting the test data to the neural network model; a process of acquiring gradient information of the test data by a back propagation process using the uncertainty as a loss; a process of generating a plurality of minutely changed test data obtained by applying a minute change to the test data and calculating deviations between each of the plurality of minutely changed test data and the test data; and a process of specifying, based on the uncertainty information, the gradient information, and the deviations, the minute change that increases or decreases the uncertainty or minutely changed test data to which the minute change is applied. 
     Further, a data-creation assistance method of the invention is realized by an information processing apparatus configured to execute: a process of storing a neural network model used for supervised machine learning and test data attached with a label of ground truth, and inputting the test data into the neural network model to specify an uncertainty of an inference result from the neural network model; a process of acquiring gradient information of the test data by a back propagation process using the uncertainty as a loss; a process of generating a plurality of minutely changed test data obtained by applying a minute change to the test data, and calculating deviations between each of the plurality of pieces minutely changed test data and the test data; and a process of specifying, based on information of the uncertainty, the gradient information, and the deviations, a minute change that increases or decreases the uncertainty or minutely changed test data to which the minute change is applied. 
     According to the invention, it is possible to efficiently verify and improve the robustness of the learning model for supervised machine learning. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a diagram illustrating a configuration example of hardware of a data-creation assistance apparatus according to the present embodiment. 
         FIG. 2  is a diagram illustrating a functional configuration example of the data-creation assistance apparatus according to the present embodiment. 
         FIG. 3  is a diagram illustrating a functional configuration example of the data-creation assistance apparatus according to a first embodiment. 
         FIG. 4  is a diagram illustrating an example of a flow of a data-creation assistance method according to the first embodiment. 
         FIG. 5  is a diagram illustrating a conceptual example of the data-creation assistance method according to the first embodiment. 
         FIG. 6  is a diagram illustrating a conceptual example of the data-creation assistance method according to the first embodiment. 
         FIG. 7  is a diagram illustrating a conceptual example of the data-creation assistance method according to the first embodiment. 
         FIG. 8  is a diagram illustrating a functional configuration example of a data-creation assistance apparatus according to a second embodiment. 
         FIG. 9  is a diagram illustrating an example of a flow of a data-creation assistance method according to the second embodiment. 
         FIG. 10  is a diagram illustrating an output example of according to the second embodiment. 
         FIG. 11  is a diagram illustrating a functional configuration example of a data-creation assistance apparatus according to a third embodiment. 
         FIG. 12  is a diagram illustrating an example of a flow of a data-creation assistance method according to the third embodiment. 
         FIG. 13  is a diagram illustrating a conceptual example of the data-creation assistance method according to the third embodiment. 
     
    
    
     DESCRIPTION OF THE PREFERRED EMBODIMENTS 
     Examples of Apparatus Configuration 
     Hereinafter, embodiments of the invention will be described in detail with reference to drawings.  FIG. 1  is a diagram illustrating a configuration example of hardware of a data-creation assistance apparatus  100  according to the present embodiment. The data-creation assistance apparatus  100  illustrated in  FIG. 1  is a computer device that efficiently verifies and improves a robustness of a learning model for supervised machine learning. 
     The hardware configuration of the data-creation assistance apparatus  100  is as follows. That is, the data-creation assistance apparatus  100  includes a storage device  101 , a memory  103 , a computing device  104 , an input device  105 , and an output device  106 . 
     The storage device  101  includes an appropriate nonvolatile storage element such as a solid-state drive (SSD) or a hard disk drive. 
     The memory  103  includes a volatile storage element such as a RAM. 
     The computing device  104  is a CPU that executes a program  102  stored in the storage device  101  by, for example, reading the program  102  into the memory  103 , and performs a general control of the device itself as well as various determinations, calculations, and control processes. 
     The program  102  according to the present embodiment includes a machine learning program  1021  in addition to an operating system (OS) naturally provided in an information processing apparatus and a program that operates on the OS and implements a data-creation assistance method. The machine learning program  1021  is an algorithm that, for example, gives appropriate data such as test data to an appropriate learned model  110  such as a neural network model to perform a predetermined determination, estimation, and the like on the data. 
     The input device  105  is an appropriate device such as a keyboard, a mouse, or a microphone that receives a key input or a voice input from a user. 
     The output device  106  is an appropriate device such as a display or a speaker that displays processed data in the computing device  104 . 
     In addition to the above-described program  102 , the storage device  101  is capable of storing the learned model  110 , test data  120 , and training data  130 . Specific contents of these will be described later. 
     Examples of Functional Configuration 
       FIG. 2  is a diagram illustrating a functional configuration example of the data-creation assistance apparatus  100  according to the present embodiment. In addition to configurations necessary as machine learning functions, such as the machine learning program  1021 , the learned model  110  (neural network model), the test data  120 , and the training data  130 , the data-creation assistance apparatus  100  according to the present embodiment further includes an uncertainty back propagation unit  111 , an uncertainty inference unit  112 , a minute change applying unit  113 , a gradient deviation comparison unit  114 , an uncertainty data display unit  115 , an uncertainty convergence determination unit  116 , and a model learning unit  117 . 
     The data-creation assistance apparatus  100  including such functional units gives the training data  130  to the machine learning program  1021  to advance learning of the learned model  110 , which is a neural network model, at any time. 
     The training data  130  is data to which a label of ground truth is attached, such as, image data in which a name or an attribute of a subject is defined as a ground truth, IoT data in which a possibility of occurrence of a failure in an equipment is defined as a ground truth. 
     Therefore, when data to be processed such as the test data is prepared and given to the learned model  110  by the machine learning program  1021 , the meanings of the data, such as “male in his thirties”, “cat”, and “failure occurrence probability 60%”, are output as estimation results. 
     Since the learned model  110  that is updated in this way is the neural network model as described above, as the estimation results for the test data  120 , in addition to information that can be uniquely specified as described above, an average and a variance of a distribution of the estimation results can be obtained as uncertainty information. 
     Therefore, the data-creation assistance apparatus  100  executes, for example, a back propagation process using a value of the variance described above as a loss, and acquires gradient data  123  of the test data  120 . An existing technique may be appropriately adopted for the back propagation process in the machine learning algorithm and a method itself for obtaining gradient data in accordance with the back propagation process. 
     Further, the data-creation assistance apparatus  100  applies predetermined minute changes to the test data  120  described above, and generates a plurality of minutely changed test data  122 . In this case, it is necessary to avoid that a direction of the minute change is different from a development intention of the user or the like or that becomes unfavorable from data characteristics (example: characters and images have completely different main shapes and change in a direction that makes it difficult for humans to recognize. For example, the presence or absence of ear images of animals having distinctive ears and changes that clearly change an outer shape, etc.). 
     Therefore, it is preferable that the data-creation assistance apparatus  100  acquires a minute change candidate vector  121  indicating the direction suitable for such minute changes by receiving a designation from the user in advance or by a predetermined method, and applies the minute change candidate vector  121  in the case of the minute change described above. This method specifies, for example, a direction, that is, a vector of the minute changes similar to the gradient indicated by the gradient data described above. 
     The data-creation assistance apparatus  100  calculates a deviation between each of the plurality of minutely changed test data  122  created as described above and the test data  120 , and acquires and stores, based on the variance (uncertainty information) of the estimation results, the gradient data  123 , and the deviations obtained so far, the minute change by which the variance (uncertainty) of the estimation results increases or decreases or the minutely changed test data to which the minute change is applied, as uncertainty test data  125 . 
     The data-creation assistance apparatus  100  generates visualization data  126  by, for example, setting information of the uncertainty test data  125  in a predetermined screen format, and displaying the visualization data  126  on the output device  106 . 
     Further, the data-creation assistance apparatus  100  uses the training data  130  (attached with the label of ground truth) stored in the storage device  101  as an input, and uses a minute change vector  131  included in the minutely changed test data  122  to generate minutely changed training data  132  obtained by applying a minute change to the training data  130 . 
     The data-creation assistance apparatus  100  further executes a relearning process of the learned model  110  (that is, the neural network model) by giving the minutely changed training data  132  to the learned model  110  by the machine learning program  1021 . 
     In this case, the data-creation assistance apparatus  100  uses the learned model  110  that has undergone the relearning to specify an evaluation value  124  of the uncertainty such as the variance described above by using a predetermined test data as an input, and determines whether the uncertainty is above or below a predetermined threshold. The data-creation assistance apparatus  100  acquires a determination result as a determination result  134  of an uncertainty convergence, and displays the determination result on, for example, the output device  106 . 
     In a case where the above uncertainty is above or below the predetermined threshold, the data-creation assistance apparatus  100  further advances the relearning process of the learned model  110  by giving the minutely changed training data  132  to the learned model  110 . 
     First Embodiment 
     Hereinafter, an actual procedure of the data-creation assistance method according to the present embodiment will be described with reference to drawings. Various operations corresponding to the data-creation assistance method described below are implemented by a program that is read by the data-creation assistance apparatus  100  into a memory or the like and executed. The program includes codes for performing the various operations described below. 
       FIG. 3  is a diagram illustrating a functional configuration example of the data-creation assistance apparatus  100  according to the first embodiment, and  FIG. 4  is a diagram illustrating an example of a flow of the data-creation assistance method according to the first embodiment. Further, actions of each function illustrated in  FIG. 3  have already been described. 
     In this case, the data-creation assistance apparatus  100  acquires, via the input device  105 , for example, by an operation of a user, the learned model  110  that is a neural network model and the test data  120  thereof (s 10 ). 
     Thereafter, the uncertainty inference unit  112  of the data-creation assistance apparatus  100  gives the test data  120  to the learned model  110 , and obtains a variance of a distribution of estimation results as the evaluation value  124  of the uncertainty of the estimation results for the test data  120  described above (s 11 ). 
     The uncertainty back propagation unit  111  of the data-creation assistance apparatus  100  executes a back propagation process using the value of the variance obtained in s 11  described above as a loss, and acquires the gradient data  123  of the test data  120  (s 12 ). 
     In addition, an existing technique may be appropriately adopted for the back propagation process in a machine learning algorithm and a method itself for obtaining gradient data in accordance with the back propagation process. 
     The minute change applying unit  113  of the data-creation assistance apparatus  100  gives a predetermined minute change to the test data  120  described above and generates the plurality of minutely changed test data  122  (s 13 ). In this case, it is necessary to avoid that a direction of the minute change is different from a development intention of the user or the like or that becomes unfavorable from data characteristics (example: characters and images have completely different main shapes and change in a direction that makes it difficult for humans to recognize. For example, the presence or absence of ear images of animals having distinctive ears and changes that clearly change an outer shape, or the like). 
     Therefore, it is assumed that the data-creation assistance apparatus  100  acquires the minute change candidate vector  121  indicating the direction suitable for such minute change by receiving a designation from the user in advance or by a predetermined method, and applies the minute change candidate vector  121  in the case of the minute change described above. 
     This method can be assumed to specify, for example, a direction, that is, a vector of the minute changes similar to a gradient indicated by the gradient data described above. 
     Next, the gradient deviation comparison unit  114  of the data-creation assistance apparatus  100  calculates deviations between each of the plurality of minutely changed test data  122  created as described above and the test data  120  and specifies and stores, based on the variance (uncertainty information) of the estimation results, the gradient data  123 , and the deviations obtained so far, the minute change by which the variance (uncertainty) of the estimation results increases or decreases or the minutely changed test data to which the minute change is given, as the uncertainty test data  125  (s 14 ), and this process is ended. 
     In this case, the gradient deviation comparison unit  114  of the data-creation assistance apparatus  100  compares, in response to specify one among the plurality of minutely changed test data  122  as the uncertainty test data  125 , each of the plurality of minutely changed test data  122  with the gradient data and identifies minutely changed test data  122  that is most similar to the gradient indicated by the gradient data. For a determination of similarity, for example, a concept may be adopted in which the inner product of each vector from each of the plurality of minutely changed test data  122  to the gradient data is calculated, and the one with the largest calculated value has the maximum similarity. 
     As such an algorithm for obtaining the average and variance values of the estimation results by the learned model  110 , a Bayesian neural network or Monte Carlo dropout sampling (MC dropout) of an approximation method thereof may be adopted. Therefore, the machine learning program  1021  in the data-creation assistance apparatus  100  is capable of mounting in advance and using such an algorithm of the MC dropout. 
     In response to specify the uncertainty shown in the flow described above or generate the minutely changed test data  122 , as shown in  FIG. 5 , first, test data X 1 , which is a base, is given to the learned model  110 , and through a process with the MC dropout described above, the average value and the variance value of an inference result obtained for the test data X 1  are identified. 
     Further, the gradient data is acquired by using the obtained variance, which is the value of the uncertainty, as a loss and executing the back propagation process related to the test data X 1 . 
     The minute change applying unit  113  of the data-creation assistance apparatus  100  applies predetermined various minute changes to the test data X 1  described above and generates a plurality of minutely changed test data X 2 . Among the plurality of minutely changed test data X 2  created in this way, a minute change by which the variance (uncertainty) of the estimation results increases or the minutely changed test data to which the minute change is given is acquired and stored as the uncertainty test data  125 . 
     Further, in response to specify the variance or the gradient data or generate the minutely changed test data, as illustrated in  FIG. 6 , it is more preferable to execute a dropout inference process a plurality of times, in this process, the test data is used as an input, an output of neurons in an intermediate layer included in the learned model  110  is randomly set to 0, and an inference result is output. 
     In this case, the data-creation assistance apparatus  100  acquires a plurality of the dropout inference results, and specifies the variance related to the plurality of dropout inference results as the uncertainty. 
     By performing such process, it is possible to effectively deal with an overfitting problem with limited resources. 
     Further, as illustrated in  FIG. 7 , the data-creation assistance apparatus  100  may assume a configuration in which a plurality of learned models  110  are stored in advance. 
     In this case, the data-creation assistance apparatus  100  acquires the inference results from each of the plurality of learned models  110  by inputting the test data to each of the plurality of learned models  110 , and acquires uncertainty data of the test data by aggregating the variances related to the inference results (for example, calculating an average of values of the variances). 
     Second Embodiment 
     Next, an example of a flow of a data-creation assistance method according to the second embodiment will be described with reference to drawings.  FIG. 8  is a diagram illustrating a functional configuration example of the data-creation assistance apparatus  100  according to the second embodiment, and  FIG. 9  is a diagram illustrating an example of a flow of the data-creation assistance method according to the second embodiment. In the configuration of  FIG. 8 , the uncertainty data display unit  115  is a difference from the configuration in the first embodiment (the configuration of  FIG. 3 ). 
     In this case, the uncertainty data display unit  115  generates visualization data  126  by, for example, setting information of the uncertainty test data  125  obtained by the gradient deviation comparison unit  114  to a screen format stored in advance (s 20 ). 
     Further, the uncertainty data display unit  115  displays the visualization data  126  described above on the output device  106  (s 21 ), and this process is ended. 
     An example of a screen G 10  on which the visualization data  126  is displayed is illustrated in  FIG. 10 . As illustrated by the screen G 10  in  FIG. 10 , the visualization data  126  includes, for example, images G 11  indicating “5” as a plurality of minutely changed test data, which are disposed on an axis plane for each direction of minute changes thereof. In this example, the axis plane is assumed in which changes in rotation angles of the images of “5” are on a horizontal axis and changes in brightness are on a vertical axis. 
     As shown in the figure, the images of “5” are rotated to the right as they move to the right, and rotated to the left as they move to the left on the axis plane. Further, the brightness of such images of “5” increase as they move upward, and decrease as they move downward on the axis plane. 
     Further, on the screen G 10 , each image G 11 , which is each piece of minutely changed test data, is associated with a variance value G 12 , which is an evaluation value of an uncertainty thereof. 
     Further, on the screen G 10 , regarding a set of each image G 11  which is each piece of minutely changed test data and each variance value G 12  of each image G 11 , in order to show that each variance value is above or below a predetermined threshold, for example, highlighting is performed to make a color or a pattern of an image G 11  different from that of another image G 11 . In the example of  FIG. 10 , a user specifies that variances of images within the range of broken lines G 13  are within an allowable range, but specific images are not within the allowable range. 
     Further, as shown on a screen G 20 , each variance value of each image G 11  may be sequentially changed and displayed in a color corresponding to a magnitude of each variance value. When such display control is performed, a heat map is displayed like the screen G 20 . 
     Third Embodiment 
     Next, an example of a flow of a data-creation assistance method according to a third embodiment will be described with reference to drawings.  FIG. 11  is a diagram illustrating a functional configuration example of a data-creation assistance apparatus  100  according to the third embodiment,  FIG. 12  is a diagram illustrating an example of a flow of the data-creation assistance method according to the third embodiment, and  FIG. 13  is a diagram illustrating an example of function outline according to the third embodiment. 
     The third embodiment corresponds to an aspect in which training data is created separately from test data. In this case, it is assumed that the data-creation assistance apparatus  100  stores, in the storage device  101 , a plurality of training data  130  used for supervised machine learning together with labels of ground truth. 
     In this case, the minute change applying unit  113  of the data-creation assistance apparatus  100  uses the training data  130  (attached with a label of ground truth) stored in the storage device  101  as an input, uses the minute change vector  131  included in the minutely changed test data  122  to generate the minutely changed training data  132  obtained by minutely changing the training data  130  (s 30 ). 
     The model learning unit  117  of the data-creation assistance apparatus  100  further executes a relearning process of the learned model  110  (that is, neural network model) by giving the minutely changed training data  132  obtained in s 30  described above to the learned model  110  by the machine learning program  1021  (s 31 ). 
     In this case, the uncertainty inference unit  112  of the data-creation assistance apparatus  100  uses the learned model  110  that has undergone the relearning to specify the evaluation value  124  of the uncertainty such as the variance described above by using a predetermined test data as an input (s 32 ). 
     In addition, the uncertainty convergence determination unit  116  determines whether the uncertainty, that is, the variance is above or below a predetermined threshold, and acquires a determination result as the determination result  134  of the uncertainty convergence (s 33 ). Each process up to this determination is, for example, repeatedly executed until the variance value reaches the threshold. 
     The uncertainty convergence determination unit  116  displays the acquired determination result  134  on, for example, the output device  106 . 
     Further, in a case where the variance, which is the uncertainty described above, is above (or below) the predetermined threshold, the model learning unit  117  of the data-creation assistance apparatus  100  further advances, the relearning process of the learned model  110  by giving the minutely changed training data  132  to the learned model  110  (S 34 ), and this process is ended. 
     Although the embodiments for carrying out the invention have been specifically described above, the invention is not limited to this, and various modifications can be made without departing from the gist thereof. 
     According to the present embodiment, it is possible to verify a robustness of a learning model with respect to specifications (various minute changes) without preparing a large amount of test data in advance, and it is possible to improve the robustness by using uncertainty data (weakness) for the learning model. 
     Namely, it is possible to efficiently verify and improve the robustness of the learning model for supervised machine learning. 
     At least the following is clarified by the description in the present description. That is, in the data-creation assistance apparatus of the present embodiment, the computing device may, in the process of specifying the uncertainty, execute a dropout inference process a plurality of times, acquire a plurality of dropout inference results, and specify a variance related to the plurality of dropout inference results as an uncertainty. In this process, the test data is used as an input, an output of neurons included in the neural network model is randomly set to 0, and an inference result is output. 
     Accordingly, it is possible to obtain information of variance by inferring efficiently and quickly, as compared with a method such as a Bayesian neural network, which tends to require a large number of resources. As a result, it is possible to more efficiently verify and improve the robustness of the learning model for supervised machine learning. 
     Further, in the data-creation assistance apparatus of the present embodiment, the computing device may, in response to acquire the gradient information, calculate a gradient of the test data by a back propagation process in which the variance of the acquired dropout inference results is used as a loss function, and in response to specify one piece of the plurality of minutely changed test data, compare the plurality of minutely changed test data with the gradient information, and specify one piece of minutely changed test data and a minute change vector that are most similar to the gradient indicated by the gradient information. 
     Accordingly, it is possible to efficiently specify and generate minutely changed test data which is similar to a tendency of the gradient and provide the test data to the learning model. As a result, it is possible to more efficiently verify and improve the robustness of the learning model for supervised machine learning. 
     Further, in the data-creation assistance apparatus of the present embodiment, the computing device may, in the process of the dropout inference, use an inference result to an intermediate layer of the neural network. 
     According to this, it is possible to measure the uncertainty with respect to a partial recognition related to an event indicated by the test data in the neural network. As a result, it is possible to more efficiently verify and improve the robustness of the learning model for supervised machine learning. 
     Further, in the data-creation assistance apparatus of the present embodiment, the computing device may, acquire inference results from each of the plurality of neural networks by inputting the test data into each of the plurality of neural network models, and specify a variance related to the inference results as the uncertainty. 
     According to this, it is expected to more efficiently measure the uncertainty for the partial recognition described above by using a huge model including a plurality of learned models as one model. As a result, it is possible to more efficiently verify and improve the robustness of the learning model for supervised machine learning. 
     Further, in the data-creation assistance apparatus of the present embodiment, the computing device may further execute a process of displaying a set of the minutely changed test data and the uncertainty information of the minutely changed test data. 
     According to this, a person in charge of managing and constructing the machine learning model is capable of obtaining visual information about directions of the minute changes in the test data and a viewpoint of the uncertainty in that case, and is capable of easily confirming the visual information. Further, a result confirmed in such a way can be fed back to a subsequent operation. As a result, it is possible to more efficiently verify and improve the robustness of the learning model for supervised machine learning. 
     Further, in the data-creation assistance apparatus of the present embodiment, the computing device may further execute a process of displaying the set of the minutely changed test data and the uncertainty information of the minutely changed test data, and displaying that the uncertainty information is above or below a predetermined threshold. 
     Accordingly, the person in charge of managing and constructing the machine learning model is capable of obtaining the visual information about directions of the minute changes in the test data and the viewpoint of the uncertainty in that case, and is capable of accurately determining the quality of the direction of the minute change and feeding the visual information back to the subsequent operation. As a result, it is possible to more efficiently verify and improve the robustness of the learning model for supervised machine learning. 
     Further, in the data-creation assistance apparatus of the present embodiment, the storage device may store a plurality of training data used for supervised machine learning together with labels of ground truth, and the computing device may further execute a relearning process of the neural network model by using the training data as an input, using the minute change vector included in the minutely changed test data to generate minutely changed training data obtained by minutely changing the training data, and giving the minutely changed training data to the neural network model. 
     According to this, the training data are also slightly changed in the same manner as the test data, so that it is possible to efficiently verify and improve the robustness. As a result, it is possible to more efficiently verify and improve the robustness of the learning model for supervised machine learning. 
     Further, in the data-creation assistance apparatus of the present embodiment, the computing device may execute a process of using the neural network model that has undergone the relearning, specifying an uncertainty by using the test data as an input, and displaying information indicating that the uncertainty is above or below the predetermined threshold; and a process of relearning of the neural network model by giving the minutely changed training data to the neural network model in a case where the uncertainty is above or below the predetermined threshold. 
     According to this, it is possible to further efficiently verify and improve the robustness of the neural network model by using the minutely changed training data in accordance with the quality of uncertainty based on the test data. As a result, it is possible to more efficiently verify and improve the robustness of the learning model for supervised machine learning.