WO2022024315A1 - 精度推定プログラム、装置、及び方法 - Google Patents

精度推定プログラム、装置、及び方法 Download PDF

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WO2022024315A1
WO2022024315A1 PCT/JP2020/029306 JP2020029306W WO2022024315A1 WO 2022024315 A1 WO2022024315 A1 WO 2022024315A1 JP 2020029306 W JP2020029306 W JP 2020029306W WO 2022024315 A1 WO2022024315 A1 WO 2022024315A1
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data set
data
accuracy
index
calculated
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French (fr)
Japanese (ja)
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友裕 早瀬
孝 河東
優 安富
健人 上村
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Fujitsu Ltd
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computing arrangements using knowledge-based models
    • G06N5/02—Knowledge representation; Symbolic representation
    • G06N5/022—Knowledge engineering; Knowledge acquisition

Definitions

  • the disclosed technology relates to an accuracy estimation program, an accuracy estimation device, and an accuracy estimation method.
  • performance verification of a trained model trained by machine learning for example, performance verification by cross validation is performed.
  • cross-validation the data set labeled with the correct answer is divided into training data, verification data, and test data. Then, the model trained with the training data is verified with the verification data, the model is designed, and the final accuracy is verified using the test data.
  • the disclosed technique aims to estimate the accuracy of the trained model for unlabeled real data.
  • the disclosed technique is a data set each containing a plurality of data in which a data value and a label are associated with each other, and acquires a plurality of data sets in which the properties of the data values are different for each data set. .. Further, the disclosed technique uses the second data as an index indicating the degree of difference between the first data set included in the plurality of data sets and the second data set included in the plurality of data sets. Calculated using the data values included in the set. The disclosed technique also calculates the accuracy of the prediction results for the second data set, predicted by the prediction model trained using the first data set.
  • the disclosed technique is based on the index and the accuracy calculated for each of a plurality of combinations of the first data set and the second data set, and the index and the prediction result by the prediction model are obtained. Identify the relevance to accuracy. Further, the disclosed technique determines the accuracy of the prediction result by the prediction model for the third data set including a plurality of data values to which the labels are not associated with the first data set and the third data set. Estimate based on the index between and the identified association.
  • it has the effect of being able to estimate the accuracy of the trained model for unlabeled real data.
  • the accuracy estimation device 10 uses the input labeled data set set to specify the relationship between the index showing the difference between the data sets and the accuracy of the prediction result of the model with respect to the data set. .. Then, the accuracy estimation device 10 estimates the accuracy of the prediction result of the model with respect to the input actual data set by using the specified relationship.
  • the accuracy estimation device 10 includes an acquisition unit 11, a learning unit 12, an index calculation unit 13, an accuracy calculation unit 14, a specific unit 15, and an estimation unit 16. .. Further, the index-accuracy curve 20 is stored in a predetermined storage area of the accuracy estimation device 10.
  • the acquisition unit 11 acquires a labeled data set set input to the accuracy estimation device 10 and passes it to the learning unit 12.
  • a labeled dataset set contains multiple labeled datasets.
  • Each labeled data set contains a plurality of data in which a data value is associated with a label representing the correct answer of the target indicated by the data value.
  • the model is a recognition model that recognizes numbers from images
  • the dataset contains a plurality of (eg, 1,000) sets of images associated with any of the labels 0-9.
  • the model is a discriminative model that identifies whether the input image is a dog image or a cat image
  • the dataset contains a plurality of sets of images associated with labels indicating dogs or cats. Is done.
  • the model is a detection model for detecting a person from an image
  • the data set includes a plurality of sets of images associated with labels indicating the existence or non-existence of the person.
  • each of the plurality of datasets included in the labeled dataset set has different properties of the data values included in each dataset.
  • the data set prepared for the recognition model that recognizes the above numbers includes a data set of a black-and-white image of handwritten numbers simply described, a data set of a black-and-white image of handwritten numbers colored, and the like. can do.
  • a dataset of images of numbers in the actual environment such as a home address tag, a dataset of composite images created by computer graphics, and images of handwritten numbers decorated or processed such as hollow characters. It can be a data set of.
  • the learning unit 12 generates a model by training using the labeled data set set passed from the acquisition unit 11.
  • the model outputs some prediction result for the actual data like the recognition model, the discriminative model, the detection model, etc. described above.
  • the model is also referred to as a "predictive model”.
  • the prediction model predicts which label the data value corresponds to by classifying the feature extractor G that extracts the feature from the data value and the feature extracted by the feature extractor G. It includes a classifier C1 that outputs the predicted result.
  • the learning unit 12 trains the parameters (weights) in the prediction model using each of the labeled data sets included in the labeled data set set. More specifically, as shown in FIG. 2, the learning unit 12 has the feature extractor G so that the label included in the data set and the prediction result by the prediction model for the data value included in the data set correspond to each other. And train each parameter of classifier C1. The learning unit 12 passes the prediction model trained for each labeled data set and the labeled data set set delivered from the acquisition unit 11 to the index calculation unit 13.
  • the index calculation unit 13 is an index showing the degree of difference between the first data set included in the labeled data set set passed from the learning unit 12 and the second data set different from the first data set. Is calculated.
  • the index calculation unit 13 calculates an index using the data values included in the data set. That is, the index calculation unit 13 calculates the index without using a label. Specifically, the index calculation unit 13 calculates an index using the prediction result of the prediction model trained using the first data set for the data values included in the second data set. By simply comparing the data values between the datasets, it is difficult to distinguish whether the datasets have different properties or the same properties but differences due to the different data itself. ..
  • the index calculation unit 13 calculates an index indicating a difference in properties between data sets by using the prediction result of the prediction model.
  • the index calculation unit 13 sets each of the combinations of the two data sets included in the labeled data set set as a pair of the data set DS and the data set DT, and calculates the index for each of all the pairs.
  • the index calculation unit 13 generates a plurality of classifiers having at least different parameters as classifiers of the prediction model trained using the data set DS. Then, as shown in the upper part of FIG. 3, the index calculation unit 13 calculates the classification error, which is the difference between the prediction results of each of the plurality of classifiers for the data set DT, as an index.
  • the index calculation unit 13 generates a classifier C2 in which the parameters of the classifier C1 of the prediction model trained using the data set DS are initialized. Then, the index calculation unit 13 calculates the classification error d (C1, C2) between the prediction result by the classifier C1 and the prediction result by the classifier C2 for the data set DT, for example, by the following equation (1).
  • the index calculation unit 13 calculates a classification error (maximum classification error, MCD, Maximum Classifier Discrepancy) maximized while optimizing the classifiers C1 and C2 as an index used in the specific unit 15 described later. For example, the index calculation unit 13 minimizes the loss function Loss shown in the following equation (2).
  • Loss ((xs, ys), xt) CrossEntryLoss (C1 (G (xs)), ys) + CrossEntryLoss (C2 (G (xs)), ys) -MeanL1Norm (C1 (G (xt))-C2 (G (xt))) (2)
  • xs is the data value of the data included in the data set DS
  • ys is the label associated with the data value xs.
  • the first term of the equation (2) is an error of the prediction result for the data set DS by the prediction model in which the classifier is the classifier C1, and corresponds to the prediction error 1 shown in the lower part of FIG.
  • the second term is an error in the prediction result for the data set DS by the prediction model in which the classifier is the classifier C2, and corresponds to the prediction error 2 shown in the lower part of FIG.
  • the third term is a classification error for the data set DT, and corresponds to, for example, the above equation (1).
  • the index calculation unit 13 optimizes the parameters of the classifiers C1 and C2 so as to minimize the loss function Loss shown in Eq. (2), and sets the third term when the loss function Loss is minimized as the maximum classification error. do.
  • the parameters of the feature extractor G are fixed.
  • FIGS. 4 and 5 show an example in which each of the classification boundary by the classifier C1, the classification boundary by the classifier C2, and the feature amount extracted from the data values included in each data set is projected in two dimensions.
  • the circle ( ⁇ ) is the feature amount of the data to which the label 0 included in the dataset DS is associated
  • the cross mark (x) is associated with the label 1 included in the dataset DS.
  • the feature amount of the collected data is shown.
  • the triangle mark ( ⁇ ) indicates the feature amount of the data included in the data set DT.
  • the classifiers C1 and C2 are used.
  • the ratio of ⁇ with different judgments that is, the ratio at which the classifier is uncertain about the judgment is considered as a classification error.
  • the classification error is 1/8, and in the example of FIG. 5, the classification error is 4/8.
  • the rate at which the classifier is uncertain about the feature quantities extracted from the data in the dataset DT should be considered to indicate the incompatibility of the feature extractor G trained in the dataset DS with the dataset DT. Can be done.
  • the classification error the more the data set DT is a data set having different properties from the data set DS for the prediction model. Therefore, in order to accurately identify the degree of ambiguity of the classifier with respect to the data set DT, the classification error is maximized.
  • FIG. 4 is an example in which the classification error is not maximized
  • FIG. 5 is an example in which the classification error is maximized. Comparing the example of FIG. 4 with the example of FIG. 5, FIG. 5 can identify ⁇ , which the classifier is confused about, as completely as possible. That is, by maximizing the classification error, a high-quality index can be calculated as an index showing the difference between the data set DS and the data set DT.
  • the index calculation unit 13 passes the maximum classification error calculated for each pair of the data set DS and the data set DT to the specific unit 15, and also passes the labeled data set set to the accuracy calculation unit 14.
  • the index calculation unit 13 calculates an index indicating the difference between the data set DS used for training the prediction model and the actual data set according to the instruction from the estimation unit 16 described later. For example, the index calculation unit 13 replaces the data set DT of the above equation (1) with an actual data set, and calculates the classification error as an index. The index calculation unit 13 passes the calculated index for the actual data set to the estimation unit 16.
  • the "actual data set” is an example of the "third data set" of the disclosed technology.
  • the accuracy calculation unit 14 calculates the accuracy of the prediction result for the data set DT predicted by the prediction model trained using the data set DS. Specifically, as shown in FIG. 6, the accuracy calculation unit 14 inputs the data set DT to the prediction model including the feature extractor G and the classifier C1. Then, the accuracy calculation unit 14 calculates the accuracy represented by, for example, the correct answer rate, based on the prediction result obtained from the prediction model and the label included in the data set DT. The accuracy calculation unit 14 also calculates the accuracy for each data set DT for which the index is calculated by the index calculation unit 13. The index calculation unit 13 passes the accuracy calculated for each data set DT to the specific unit 15.
  • the specifying unit 15 identifies the relationship between the difference between the data sets and the accuracy of the prediction result by the prediction model based on the index and the accuracy calculated for each combination of the data set DS and the data set DT. .. Specifically, as shown in FIG. 7, the specific unit 15 is an index calculated for each pair of the data set DS and the data set DT in a space having an index on the horizontal axis and accuracy on the vertical axis. The points corresponding to the maximum classification error and the accuracy (black circles in FIG. 7) are plotted. Based on the plotted points, the specific unit 15 obtains, for example, a regression curve (solid line curve in FIG. 7) showing an estimated value by Bayesian estimation or the like. Hereinafter, this regression curve is referred to as "index-precision curve 20".
  • the index-accuracy curve 20 and the 95% confidence interval (shaded portion in FIG. 7) with respect to the estimated value are also shown.
  • the relationship between the index showing the difference between the data sets and the accuracy of the prediction result by the prediction model is a relationship in which the accuracy decreases monotonically as the maximum classification error, which is an index, increases.
  • the specific unit 15 stores the information of the obtained index-precision curve 20 in a predetermined storage area.
  • the estimation unit 16 determines the accuracy of the prediction result by the prediction model for an actual data set containing a plurality of data whose data values are not associated with labels, and an index showing the difference between the data set DS and the actual data set, and an index-accuracy. Estimate based on curve 20.
  • a real dataset is a dataset of data values acquired in the real environment to which the predictive model is applied.
  • the estimation unit 16 acquires the actual data set and passes it to the index calculation unit 13, and also instructs the index calculation unit 13 to calculate the classification error as an index for the actual data set, and the index calculation unit 13. From 13, we receive an index for the actual data set. Then, the estimation unit 16 refers to the index-accuracy curve 20 and acquires an estimated value of accuracy corresponding to the index for the actual data set, as shown in FIG. The estimation unit 16 outputs the acquired estimated value as an accuracy estimation result.
  • the parameter of the classifier C1 of the prediction model in the actual environment may be a randomly initialized value.
  • the feature extractor G is an essential part, and the classifier C1 has a shallow structure of, for example, one or two layers. Therefore, the difference between the parameters of the classifier C1 in the actual environment and the parameters of the classifier C1 when the index-accuracy curve 20 is obtained does not greatly affect the estimation of the accuracy.
  • the accuracy estimation device 10 can be realized by, for example, the computer 40 shown in FIG.
  • the computer 40 includes a CPU (Central Processing Unit) 41, a memory 42 as a temporary storage area, and a non-volatile storage unit 43. Further, the computer 40 includes an input / output device 44 such as an input unit and a display unit, and an R / W (Read / Write) unit 45 that controls reading and writing of data to the storage medium 49. Further, the computer 40 includes a communication I / F (Interface) 46 connected to a network such as the Internet.
  • the CPU 41, the memory 42, the storage unit 43, the input / output device 44, the R / W unit 45, and the communication I / F 46 are connected to each other via the bus 47.
  • the storage unit 43 can be realized by an HDD (Hard Disk Drive), an SSD (Solid State Drive), a flash memory, or the like.
  • the storage unit 43 as a storage medium stores an accuracy estimation program 50 for causing the computer 40 to function as the accuracy estimation device 10.
  • the accuracy estimation program 50 includes an acquisition process 51, a learning process 52, an index calculation process 53, an accuracy calculation process 54, a specific process 55, and an estimation process 56.
  • the storage unit 43 has an information storage area 60 in which information constituting the index-precision curve 20 is stored.
  • the CPU 41 reads the accuracy estimation program 50 from the storage unit 43, expands it into the memory 42, and sequentially executes the processes of the accuracy estimation program 50.
  • the CPU 41 operates as the acquisition unit 11 shown in FIG. 1 by executing the acquisition process 51. Further, the CPU 41 operates as the learning unit 12 shown in FIG. 1 by executing the learning process 52. Further, the CPU 41 operates as the index calculation unit 13 shown in FIG. 1 by executing the index calculation process 53. Further, the CPU 41 operates as the accuracy calculation unit 14 shown in FIG. 1 by executing the accuracy calculation process 54. Further, the CPU 41 operates as the specific unit 15 shown in FIG. 1 by executing the specific process 55. Further, the CPU 41 operates as the estimation unit 16 shown in FIG. 1 by executing the estimation process 56. Further, the CPU 41 reads information from the information storage area 60 and expands the index-precision curve into the memory 42. As a result, the computer 40 that has executed the accuracy estimation program 50 functions as the accuracy estimation device 10.
  • the CPU 41 that executes the program is hardware.
  • the function realized by the accuracy estimation program 50 can also be realized by, for example, a semiconductor integrated circuit, more specifically, an ASIC (Application Specific Integrated Circuit) or the like.
  • a semiconductor integrated circuit more specifically, an ASIC (Application Specific Integrated Circuit) or the like.
  • the accuracy estimation device 10 executes the identification process shown in FIG. Further, when the actual data set is input to the accuracy estimation device 10 and the accuracy estimation is instructed, the accuracy estimation device 10 executes the estimation process shown in FIG.
  • the specific process and the estimation process are examples of the accuracy estimation method of the disclosed technology. Hereinafter, each of the specific processing and the estimation processing will be described in detail.
  • step S11 the acquisition unit 11 selects two data sets from the labeled data set set input to the accuracy estimation device 10, acquires them as a pair of the data set DS and the data set DT, and transfers the data set to the learning unit 12. Hand over.
  • step S12 the learning unit 12 configures the prediction model so that the label included in the dataset DS and the prediction result of the prediction model for the data value included in the dataset DS correspond to each other. Train each parameter of G and classifier C1.
  • step S13 the index calculation unit 13 generates a classifier C2 in which the parameters of the classifier C1 of the prediction model trained using the data set DS are initialized. Then, the index calculation unit 13 calculates the classification error between the prediction result by the classifier C1 and the prediction result by the classifier C2 for the data set DT. Further, the index calculation unit 13 calculates the maximum classification error that maximizes the classification error while optimizing the classifiers C1 and C2.
  • step S14 the accuracy calculation unit 14 inputs the data set DT to the prediction model, and based on the prediction result obtained from the prediction model and the label included in the data set DT, for example, a table with a correct answer rate or the like. Calculate the accuracy to be done.
  • the accuracy calculation unit 14 temporarily stores the calculated accuracy in a predetermined storage area together with the index calculated in step S13.
  • step S15 the acquisition unit 11 determines whether or not the processing of steps S11 to S14 has been completed for all the pairs of the data sets included in the labeled data set set. If there are unprocessed pairs, the process returns to step S11, and if the process is completed for all the pairs, the process proceeds to step S16.
  • step S16 the specific unit 15 is calculated for each pair of the data set DS and the data set DT once stored in a predetermined storage area in a space having an index on the horizontal axis and accuracy on the vertical axis. Plot the points corresponding to the maximum classification error, which is an index, and the accuracy. Then, the specifying unit 15 specifies, for example, a regression curve showing an estimated value by Bayesian inference or the like as an index-precision curve 20 based on the plotted points. The identification unit 15 stores the information of the specified index-precision curve 20 in a predetermined storage area, and the identification process ends.
  • step S21 the estimation unit 16 acquires the actual data set and passes it to the index calculation unit 13, and also instructs the index calculation unit 13 to calculate the classification error as an index for the actual data set.
  • step S22 the index calculation unit 13 calculates the classification error as an index showing the difference between the data set DS used for training the prediction model and the actual data set, and uses the calculated index for the actual data set. Hand over to the estimation unit 16.
  • step S23 the estimation unit 16 refers to the index-accuracy curve 20 to acquire an estimated value of accuracy corresponding to the index for the actual data set, and outputs the acquired estimated value as the accuracy estimation result. .. Then, the estimation process ends.
  • the accuracy estimation device acquires a plurality of data sets having different data value properties for each data set, and for each pair of the data set DS and the data set DT, between the data sets. Calculate an index showing the degree of difference between. As an index, the maximum classification error that maximizes the classification error indicating the difference in the prediction result by each of the plurality of classifiers for the data set DT while optimizing the plurality of classifiers is calculated. In addition, the accuracy estimation device calculates the accuracy of the prediction result for the data set DT predicted by the prediction model trained using the data set DS.
  • the accuracy estimation device determines the relationship between the difference between the data sets and the accuracy of the prediction result by the prediction model based on the index and the accuracy calculated for each of a plurality of pairs of the data set DS and the data set DT. Identify.
  • the accuracy estimator determines the accuracy of the prediction results for the actual dataset by the prediction model trained using the dataset DS, based on the classification error between the dataset DS and the actual dataset and the identified association. To estimate. This allows the accuracy of the trained model to be estimated for unlabeled real data.
  • the index-accuracy curve is specified and used to estimate the accuracy of the actual data set. This makes it possible to quantitatively estimate how much the accuracy of the predictive model will decrease with respect to changes in the properties between the data sets due to the difference between the environment and the actual environment during the predictive model training.
  • the maximum classification error can be calculated by minimizing the loss function Loss by an iterative algorithm.
  • the number of repetitions of this repetition algorithm may be limited so that the repetition algorithm is stopped early.
  • the relationship between the maximum classification error and the accuracy as shown by the broken line in FIG. 12, it is desirable that the accuracy does not change abruptly with respect to the fluctuation of the maximum classification error.
  • the maximum classification error may be the same value for any data set. In this case, as shown by the solid line in FIG. 12, the relationship between the maximum classification error and the accuracy drops sharply at the place where the maximum classification error is large, even though the fluctuation of the maximum classification error is small. (One-dot chain line portion in FIG. 12).
  • the accuracy estimation value fluctuates greatly at the place where the maximum classification error is large, and stable accuracy estimation cannot be performed. Therefore, the iterative algorithm is stopped early so that the index-precision curve has the desired variation as shown by the broken line in FIG.
  • the number of repetitions in the case of early stop may be specified and set in advance by an experiment or the like so that the maximum classification error for different data sets is separated by a predetermined value or more. The number of repetitions for early stop is the same for all dataset pairs.
  • two or more data sets included in the labeled data set set may be combined to generate a new data set.
  • the accuracy of the data set DT for the prediction model is used as the accuracy used in the index-accuracy curve
  • it may be a value indicating the degree of decrease in the accuracy of the data set DT, such as the difference between the accuracy of the data set DS with respect to the prediction model and the accuracy of the data set DT with respect to the prediction model.
  • the mode in which the accuracy estimation program is stored (installed) in the storage unit in advance has been described, but the present invention is not limited to this.
  • the program according to the disclosed technology can also be provided in a form stored in a storage medium such as a CD-ROM, a DVD-ROM, or a USB memory.

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