Patent Publication Number: US-11651268-B2

Title: Model update support system

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is based upon and claims the benefit of priority from Japanese Patent Application No. 2018-171824, filed on Sep. 13, 2018; the entire contents of which are incorporated herein by reference. 
     FIELD 
     Embodiments described herein relate generally to a model update support system. 
     BACKGROUND 
     A model that is trained using deep learning is used to classify data, etc. It is desirable to appropriately update the model to continuously classify data with high accuracy. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    is a schematic view illustrating a configuration of a model update support system according to a first embodiment; 
         FIG.  2    is a schematic view illustrating an output according to the model update support system according to the first embodiment; 
         FIG.  3    is a flowchart illustrating processing using the model update support system according to the first embodiment; 
         FIG.  4    and  FIG.  5    are schematic views illustrating output according to the model update support system according to the first embodiment; and 
         FIG.  6    is a schematic view illustrating the configuration of a model update support system according to a second embodiment. 
     
    
    
     DETAILED DESCRIPTION 
     According to one embodiment, a model update support system supports an update of a first model trained using a training data group. The training data group includes a plurality of labeled data and includes a plurality of labels respectively labeling the plurality of labeled data. The system includes a processor. The processor is configured to output first information or second information based on a classification certainty and a plurality of similarities. The classification certainty is calculated using the first model and indicates a sureness of a classification of first data. The plurality of similarities respectively indicate likenesses between the first data and the plurality of labeled data. The first information indicates that the training of the first model is insufficient. The second information indicates that one of the plurality of labels is inappropriate. 
     Embodiments of the invention will now be described with reference to the drawings. 
     In the drawings and the specification of the application, components similar to those described thereinabove are marked with like reference numerals, and a detailed description is omitted as appropriate. 
       FIG.  1    is a schematic view illustrating a configuration of a model update support system according to a first embodiment. 
     The model update support system  110  according to the first embodiment illustrated in  FIG.  1    is used to support an update of a trained model. 
     For example, data may be classified using a trained model. If the model is trained sufficiently and appropriately for each of the classifications, the model can infer the classifications of the input data more accurately. 
     However, there are cases where the model is trained insufficiently or the model is trained inappropriately for one classification. In such a case, data that should be inferred as being in one classification may be inferred as being in another classification. Or, the data may be inferred as being in a classification with a low classification certainty. 
     The classification certainty is a value calculated when the model classifies the data. The classification certainty indicates the sureness of the inferred classification. The likelihood of the classification of the data inferred by the model matching the actual classification increases as the classification certainty increases. 
     Hereinafter, “anomalous” data refers to the data for which the correct classification could not be inferred by the model, or the data for which the correct classification can be inferred but the classification certainty is low. The data for which the correct classification can be inferred by the model with a high classification certainty is called “normal” data. 
     It is desirable to update (retrain) the model if anomalous data occurs when the model classifies the data. However, the anomaly often is caused by the training data used to train the model or the internal structure of the model. Therefore, it is not easy for the user to discriminate the cause of the anomaly. 
     The model update support system  110  is used to provide the user with information relating to the cause of the anomaly and to support the update of the model. The user can understand how to best update the model based on the information provided by the model update support system  110 . 
     The model update support system  110  according to the embodiment includes a processor  10 . As illustrated in  FIG.  1   , the model update support system  110  may further include an acquirer  20 , an outputter  30 , model memory  51 , and training data memory  52 . 
     The acquirer  20  acquires information such as images, voice, etc., as digital data and outputs the digital data to the processor  10 . The acquirer  20  includes, for example, at least one of an imaging device or a microphone. The acquirer  20  may store the acquired information in a not-illustrated memory. In such a case, the processor  10  refers to the acquired data by accessing the memory. 
     The processor  10  includes, for example, a CPU (Central Processing Unit), an electronic circuit, etc. The processor  10  includes a receiver  11 , a classification certainty calculator  12 , a determiner  13 , a similarity calculator  14 , and a cause selector  15 . 
     For example, the acquirer  20  acquires first data by imaging or voice recording. The receiver  11  receives the first data output from the acquirer  20 . When the receiver  11  receives the first data, the classification certainty calculator  12  accesses the model memory  51  and the training data memory  52 . 
     The model memory  51  stores a trained first model. The training data memory  52  stores a training data group used to train the first model. The training data group includes multiple training data. Each of the training data includes one labeled datum and one label indicating the classification of the labeled datum. 
     The model memory  51  and the training data memory  52  include storage media such as hard disk drives, flash memory, network hard disks, etc. One storage medium may function as the model memory  51  and the training data memory  52 . 
     The classification certainty calculator  12  inputs the first data to the first model and causes the first model to infer the classification of the first data. The classification certainty calculator  12  calculates a first classification certainty based on the output of the inference by the first model. The first classification certainty indicates the sureness of the classification (a first classification) of the first data inferred from the first model. 
     Further, the classification certainty calculator  12  calculates multiple classification certainties by sequentially inputting multiple labeled data to the first model. The classification certainty calculator  12  outputs the first data, the first classification certainty, and the multiple classification certainties based on the multiple labeled data to the determiner  13 . 
     The determiner  13  determines whether or not the first classification certainty is sufficiently high based on the first classification certainty and the multiple classification certainties. For example, the determiner  13  calculates the average value and the fluctuation of the multiple classification certainties and sets a threshold using the average value and the fluctuation. The determiner  13  compares the first classification certainty to the threshold that is set. In the case where the first classification certainty is not less than the threshold, the determiner  13  determines that the first data is normal. This means that the likelihood is high that the first classification certainty is sufficiently high and the classification of the first data is inferred correctly. 
     The method for setting the threshold is not limited to the example. A value that is preset by the user may be used as the threshold without using the multiple classification certainties to set the threshold. In such a case, it is unnecessary to perform the calculation of the multiple classification certainties by the classification certainty calculator  12 , the calculation of the average value and the fluctuation by the determiner  13 , etc. 
     The first classification certainty being less than the threshold means that the first data is anomalous. In such a case, the determiner  13  outputs the first data to the similarity calculator  14  and outputs, to the cause selector  15 , the multiple classification certainties relating to the multiple labeled data. 
     The similarity calculator  14  calculates multiple similarities by using the first data and the multiple labeled data. The multiple similarities respectively indicate the likenesses between the first data and the multiple labeled data. The similarity calculator  14  outputs the calculated multiple similarities to the cause selector  15 . 
     The cause selector  15  is configured to select first information or second information based on the multiple similarities. A case is described in the example where the cause selector  15  appropriately selects the first information or the second information based on at least a part of the multiple classification certainties and the multiple similarities. The first information indicates that the training of the model is insufficient. The second information indicates that one of the labels included in the training data group is inappropriate. 
     In the case where the cause selector  15  selects the first information or the second information, the cause selector  15  outputs the selected information to the outputter  30 . There are also cases where the cause selector  15  does not select the first information or the second information based on the multiple classification certainties and the multiple similarities. 
     The outputter  30  outputs the first information or the second information so that the user can recognize the first information or the second information. The outputter  30  includes at least one of a monitor, a speaker, or a printer. For example, the outputter  30  includes a monitor or a printer and outputs the first information or the second information to be viewable. The outputter  30  may output other information with the first information or the second information. The other information may be the first data, the first classification, the first classification certainty, first labeled data similar to the first data, the classification of the first labeled data, the classification certainty of the first labeled data, a label determined to be inappropriate, second labeled data labeled with the label determined to be inappropriate, etc. 
     According to the model update support system  110  according to the first embodiment, in the case where the first data is anomalous, information that indicates the cause of the anomaly can be provided to the user. The user can update the first model based on the provided information. For example, when the training of the first model relating to the first classification is insufficient, the first model is retrained for the first classification. When the label is mistaken, the first model is retrained using training data having the corrected label. Thereafter, the data can be classified with higher accuracy using the first model. 
     An example of the processing relating to the model update support system  110  will now be described more specifically. 
     For example, the first model is generated by the following method. First, deep learning is applied to the untrained model for a task such as classifying input data into each type. Then, pre-training is performed by inputting labeled data that is not labeled. Subsequently, fine tuning is performed for each type of the data by using taught labeled data. The data that is to be classified is input to the learning model that is generated; and classifying (labeling) by deep learning is performed. 
     The classification certainty calculator  12  inputs the first data to the trained first model and acquires an output vector from the first model. The classification certainty calculator  12  inputs the output vector into a softmax function and infers, as the classification of the first data, the classification in the output vector of the softmax function for which the maximum value is obtained. Also, the maximum value is used as the classification certainty. 
     The determiner  13  acquires the multiple classification certainties calculated by the classification certainty calculator  12  by sequentially inputting the multiple labeled data included in the training data group to the first model. The determiner  13  determines whether the first data is normal or anomalous by comparing the first classification certainty (x) of the first data to the average (μ) and the variance (σ) of the classification certainties of the multiple labeled data. For example, the determiner  13  determines that the first data is anomalous when the following Formula 1 holds. α is a preset coefficient.
 
 x −(μ−α*σ)&lt;0.0  [Formula 1]
 
     The similarity calculator  14  calculates multiple similarities respectively between the first data and the multiple labeled data. For example, the similarity calculator  14  calculates the similarity based on a Euclidean distance d represented by the following Formula 2. For example, the first data and the labeled data are more similar as the value of the similarity between these data increases. Other than the Euclidean distance, cosine similarity or the like may be used to calculate the similarity. 
     
       
         
           
             
               
                 
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     In Formula 2, p=(p 1 , p 2 , . . . , pi) is the output vector of the layer one-previous to the final layer when inferring the first data. q=(q 1 , q 2 , . . . , qi) is the output vector of the layer one-previous to the final layer when inferring the labeled data using the first model. Or, the output vector of the layer at least two-previous to the final layer may be used as the output vector for the first data and for the labeled data; or the output vector of the final layer may be used. 
     The cause selector  15  is configured to select the first information or the second information. 
     For example, the first information includes first detailed information and second detailed information recited below. The first detailed information indicates that training data relating to the first classification of the first data was not available when training the first model. The second detailed information indicates that the training data relating to the first classification was available when training the first model, but the training relating to the first classification was insufficient. 
     The cause selector  15  selects the first detailed information in the case where the first condition is satisfied. The first condition is when the maximum value of the multiple similarities falls below a first threshold. The maximum similarity between the first data and one example datum indicates that among the training data, the one example datum is most similar to the first data. The maximum value of the similarities being less than the first threshold indicates that the labeled data of the training data group that is most similar to the first data does not resemble the first data. This indicates that the training data when training the first model did not include data resembling the first data (data belonging to the first classification). 
     When the first condition is not satisfied, the cause selector  15  extracts multiple similar data from the multiple labeled data. The multiple similar data includes the first labeled data for which the maximum value of the multiple similarities is obtained. The multiple similar data is data among the multiple labeled data that is relatively similar to the first data. The cause selector  15  refers to multiple reference certainties respectively indicating the surenesses of the classifications of the multiple similar data. The multiple reference certainties are a part of the multiple classification certainties calculated by the classification certainty calculator  12 . 
     The cause selector  15  calculates the average value and the fluctuation of the multiple reference certainties. In the case where a second condition is satisfied, the cause selector  15  selects the second detailed information. The second condition is when the average value is less than a second threshold or when the fluctuation is a third threshold or more. The second condition may be when the average value is less than the second threshold and the fluctuation is the third threshold or more. 
     The maximum value of the similarities being the first threshold or more indicates that data similar to the first data is included in the training data group. On the other hand, the average value of the multiple reference certainties being less than the second threshold or the fluctuation being the third threshold or more indicates that the first model is not trained sufficiently for the first data. In other words, this indicates that labeled data similar to the first data (labeled data belonging to the first classification) was insufficiently included in the training data group when training the first model. 
     In the case where neither the first condition nor the second condition is satisfied, the cause selector  15  determines that an inappropriate label included in the training data group is the cause of the anomaly; and the cause selector  15  selects the second information. 
     Or, in the case where neither the first condition nor the second condition is satisfied, the cause selector  15  refers to the multiple classifications of the multiple similar data inferred from the first model. For each of the multiple similar data, the cause selector  15  compares the multiple classifications with the multiple labels respectively labeling the multiple similar data. The cause selector  15  selects the second information when one of the multiple labels and one of the multiple classifications do not match for one similar data. When the multiple classifications and the multiple labels respectively match, the cause selector  15  does not select any information; and the processing ends. 
       FIG.  2    is a schematic view illustrating an output according to the model update support system according to the first embodiment. 
       FIG.  3    is a flowchart illustrating processing using the model update support system according to the first embodiment. 
       FIG.  4    and  FIG.  5    are schematic views illustrating output according to the model update support system according to the first embodiment. 
     Here, an example is described in which images of dogs are input to the first model; and the first model is caused to infer the dog breed. In the example, the outputter  30  is a monitor. 
     For example, in the case where the first data is determined by the determiner  13  to be normal, the processor  10  causes the outputter  30  to display the first classification certainty and the first classification of the first data inferred from the first model. 
       FIG.  2    shows an output example when the first data is normal. In the example illustrated in  FIG.  2   , the processor  10  displays first data a 1 , a first classification a 2 , a first classification certainty a 3 , certainties a 4  of the classifications relating to the first data, one or more labeled data a 5  similar to the first data, classifications a 6  of the labeled data inferred from the first model, classification certainties a 7 , labels a 8  labeling the labeled data, certainties a 9  of the classifications relating to the labeled data, etc. 
     When the input data is determined by the determiner  13  to be anomalous, the multiple similarities between the data and the multiple labeled data are input to the cause selector  15 ; and the processing of the flowchart illustrated in  FIG.  3    is started. The cause selector  15  causes the outputter  30  to display the first data determined to be anomalous and the classification certainties of the classifications relating to the first data (step S 1 ). 
     The user confirms the first data displayed by the outputter  30  and determines whether or not a discrepancy is in the data based on the appearance (step S 2 ). An example of a discrepancy of the appearance is when the entire screen is blurred and the image itself cannot be recognized, etc. When there is a discrepancy based on the appearance, the cause of the anomalous data determination is determined to be an imaging discrepancy (step S 3 ). 
     In the case where there is no discrepancy based on the appearance, the cause selector  15  causes the outputter  30  to display labeled data having a relatively high similarity with the first data (step S 4 ). The user determines whether or not the displayed labeled data resembles the first data (step S 5 ). 
     In the case where the displayed labeled data does not resemble the first data, the cause selector  15  determines that the cause of the anomaly is insufficient training of the first model (step S 6 ); and the cause selector  15  selects the first detailed information. In other words, it is determined that training of the first model relating to the first data was not performed. 
     Instead of the user determining whether or not the labeled data resembles the first data, the cause selector  15  may perform the determination using the similarity. For example, as described above, the cause selector  15  determines whether or not the first condition is satisfied. In the case where the first condition is satisfied, the cause selector  15  selects the first detailed information. 
     In the case where the displayed labeled data resembles the first data (the first condition is not satisfied), the cause selector  15  determines whether or not the second condition is satisfied as described above (step S 7 ). In the case where the second condition is satisfied, the cause selector  15  determines that the cause of the anomaly is insufficient training of the first model (step S 8 ); and the cause selector  15  selects the second detailed information. More specifically, it is determined that the training of the first model relating to the first data was performed; but the training was insufficient. 
     In the case where the second condition is not satisfied, the cause selector  15  determines, for each of the multiple similar data, whether or not the labels labeling the multiple similar data match the classifications of the multiple similar data inferred from the first model (step S 9 ). In the case where the label does not match, the cause selector  15  determines that the cause of the anomaly is a mistaken label (step S 10 ); and the cause selector  15  selects the second information. In the case where the label matches, the cause selector  15  determines that the anomaly is not a problem (step S 11 ); and the processing ends. 
     In the case where the cause selector  15  selects at least some information in the processing recited above, the cause selector  15  causes the outputter  30  to display the information.  FIG.  4    illustrates the case where the cause selector  15  causes the outputter  30  to display the first information and the second information. 
     In the example of  FIG.  4   , first data b 1 , a first classification b 2 , a first classification certainty b 3 , and certainties b 4  of the classifications relating to the first data are displayed for the first data. As the first information, labeled data c 1  that is similar to the first data, a classification c 2  of the labeled data c 1  inferred from the first model, a classification certainty c 3  that indicates the sureness of the classification c 2 , a label c 4  that labels the labeled data c 1 , certainties c 5  of the classifications relating to the labeled data c 1 , first information c 6 , etc., are displayed. As the second information, labeled data d 1  for which a mismatch is discovered between the label and the classification, a classification d 2  of the labeled data d 1  inferred from the first model, a classification certainty d 3  that indicates the sureness of the classification d 2 , a label d 4  that labels the labeled data d 1 , certainties d 5  of the classifications relating to the labeled data d 1 , second information d 6 , etc., are displayed. 
     The region where the labeled data relating to the first information or the second information is displayed may be set to be discriminable from the region where the other data is displayed. In the example illustrated in  FIG.  4   , patterns that are discriminable from those of the other data are displayed in the regions where the labeled data c 1  and the labeled data d 1  are displayed. Mutually-different colors, etc., may be provided instead of the patterns. 
     The processor  10  also may cause the outputter  30  to output other information. For example, the processor  10  may cause the outputter  30  to display a saliency map showing which part of the data is being responded to when inferring. The processor  10  may separately display R, G, and B of the image of the first data. Also, the image of the first data and the saliency map may be displayed superimposed. 
     Here, a case is described where one datum is input to the processor  10 . Multiple data (e.g., multiple images) may be input to the processor  10 . In such a case, a histogram of the classifications such as that illustrated in  FIG.  5    may be presented for the initial input of the multiple data. Thereby, the user can determine that there is no bias by classification. If a bias exists, the likelihood of the screening being performed prejudicially, etc., can be detected. 
     As described above, according to the model update support system including the processor  10  including the cause selector  15 , the first information that indicates insufficient training of the first model or the second information that indicates an inappropriateness of one of the multiple labels can be output based on the classification certainty indicating the sureness of the classification of the first data calculated using the first model and based on the multiple similarities respectively indicating the likenesses between the first data and the multiple labeled data. By providing the anomaly cause information to the user, the update of the first model is easy. 
     In the example illustrated in  FIG.  1   , other than the cause selector  15 , the processor  10  includes the receiver  11 , the classification certainty calculator  12 , the determiner  13 , and the similarity calculator  14 . This is not limited to the example; and the processor  10  may not include components other than the cause selector  15 . For example, the similarities and/or the classification certainties may be calculated by another processor; and the calculation results may be input to the processor  10 . 
       FIG.  6    is a schematic view illustrating the configuration of a model update support system according to a second embodiment. 
     In the model update support system  210  according to the second embodiment illustrated in  FIG.  6   , the processor  10  further includes a labeler  16  and an updater  17 . The model update support system  210  further includes an inputter  40 . 
     The processing of the receiver  11 , the classification certainty calculator  12 , the determiner  13 , the similarity calculator  14 , and the cause selector  15  of the processor  10  is similar to that of the model update support system  110 . For example, the first information or the second information is selected by the cause selector  15 ; and the information is output from the outputter  30 . The user operates the inputter  40  by referring to the first information or the second information that is output. 
     The inputter  40  includes at least one of a keyboard, a mouse, a touch panel, or a microphone (a voice operation). 
     For example, when the first information is output, the user performs an operation to add, to the training data memory  52 , training data relating to the original classification of the first data determined to be anomalous. When the second information is output, the user performs an operation of inputting the correct label. When the user inputs the label, the labeler  16  labels the labeled data related to the second information with the label input from the user. The labeler  16  stores the labeled data and the label in the training data memory  52 . 
     When the training data group of the training data memory  52  is modified, the updater  17  updates (retrains) the first model of the model memory  51  by using the modified training data group. The modification of the training data group includes the addition of training data, the correction of a label, etc. The updater  17  stores the updated first model in the model memory  51 . 
     By including the labeler  16  and the updater  17 , the processor  10  not only can provide the anomaly cause information to the user but also can perform the update of the first model to improve the anomaly. Thereby, the convenience of the user can be improved. 
     An example is illustrated in  FIG.  6    in which one processor functions as the receiver  11 , the classification certainty calculator  12 , the determiner  13 , the similarity calculator  14 , the cause selector  15 , the labeler  16 , and the updater  17 . This is not limited to the example; and these functions may be realized by multiple processors. For example, one processor may function as the receiver  11 , the classification certainty calculator  12 , the determiner  13 , the similarity calculator  14 , and the cause selector  15 ; and another processor may function as the labeler  16  and the updater  17 . A system that includes such processors can be considered to substantially include the processor  10 . 
     According to the embodiments described above, a model update support system can be provided in which information indicating the cause of an anomaly can be output. 
     For example, the processing of the various data recited above is executed based on a program (software). For example, the processing of the various information recited above is performed by a computer storing the program and reading the program. 
     The processing of the various information recited above may be recorded in a magnetic disk (a flexible disk, a hard disk, etc.), an optical disk (CD-ROM, CD-R, CD-RW, DVD-ROM, DVD±R, DVD±RW, etc.), semiconductor memory, or another recording medium as a program that can be executed by a computer. 
     For example, the information that is recorded in the recording medium can be read by a computer (or an embedded system). The recording format (the storage format) of the recording medium is arbitrary. For example, the computer reads the program from the recording medium and causes a CPU to execute the instructions described in the program based on the program. In the computer, the acquisition (or the reading) of the program may be performed via a network. 
     At least a part of the processing of the information recited above may be performed by various software operating on a computer (or an embedded system) based on a program installed in the computer from a recording medium. The software includes, for example, an OS (operating system), etc. The software may include, for example, middleware operating on a network, etc. 
     The recording medium according to the embodiments stores a program that can cause a computer to execute the processing of the various information recited above. The recording medium according to the embodiments also includes a recording medium to which a program is downloaded and stored using a LAN, the Internet, etc. The processing recited above may be performed based on multiple recording media. 
     The computer according to the embodiments includes one or multiple devices (e.g., personal computers, etc.). The computer according to the embodiments may include multiple devices connected by a network. 
     Hereinabove, embodiments of the invention are described with reference to specific examples. However, the invention is not limited to these specific examples. For example, one skilled in the art may similarly practice the invention by appropriately selecting specific configurations of components such as the processor, the acquirer, the outputter, the inputter, the memory, etc., from known art; and such practice is within the scope of the invention to the extent that similar effects can be obtained. 
     Further, any two or more components of the specific examples may be combined within the extent of technical feasibility and are included in the scope of the invention to the extent that the purport of the invention is included. 
     Moreover, all model update support systems practicable by an appropriate design modification by one skilled in the art based on the model update support systems described above as embodiments of the invention also are within the scope of the invention to the extent that the spirit of the invention is included. 
     Various other variations and modifications can be conceived by those skilled in the art within the spirit of the invention, and it is understood that such variations and modifications are also encompassed within the scope of the invention. 
     While certain embodiments have been described, these embodiments have been presented by way of example only, and are not intended to limit the scope of the inventions. Indeed, the novel embodiments described herein may be embodied in a variety of other forms; furthermore, various omissions, substitutions and changes in the form of the embodiments described herein may be made without departing from the spirit of the inventions. The accompanying claims and their equivalents are intended to cover such forms or modifications as would fall within the scope and spirit of the invention.