Abstract
Data drift is a change in the distribution of data during machine learning model training and during operation. This occurs regardless of the type of data and adversely affects model performance. However, this method can only analyze the drift detection results from the difference in the distribution of the output of a two-sample test, and does not analyze the actual degradation of the prediction performance of the machine learning model due to drift. In addition to class probability, we believe that detecting drift for changes in the local region that the model is actually gazing at will improve accuracy. In this study, we propose a drift detection method based on the Attention Branch Network (ABN), which enables visualization of the basis of judgment in image classification. In our method, drift is detected using the class probabilities output by the attention branch and perception branch, which constitute the ABN, and the attention map. The results show that the detection rate can be improved by introducing an attention map to drift detection in addition to class probability. We also observed that the attention map tended to shrink with drift.
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Nitta, T., Shi, Y., Hirakawa, T., Yamashita, T., Fujiyoshi, H. (2023). Detecting Data Drift with KS Test Using Attention Map. In: Lu, H., Blumenstein, M., Cho, SB., Liu, CL., Yagi, Y., Kamiya, T. (eds) Pattern Recognition. ACPR 2023. Lecture Notes in Computer Science, vol 14406. Springer, Cham. https://doi.org/10.1007/978-3-031-47634-1_6
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DOI: https://doi.org/10.1007/978-3-031-47634-1_6
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