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arXiv · 2205.07116

Practical Insights of Repairing Model Problems on Image Classification

Akihito Yoshii·Susumu Tokumoto·Fuyuki Ishikawa
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Abstract

Additional training of a deep learning model can cause negative effects on the results, turning an initially positive sample into a negative one (degradation). Such degradation is possible in real-world use cases due to the diversity of sample characteristics. That is, a set of samples is a mixture of critical ones which should not be missed and less important ones. Therefore, we cannot understand the performance by accuracy alone. While existing research aims to prevent a model degradation, insights into the related methods are needed to grasp their benefits and limitations. In this talk, we will present implications derived from a comparison of methods for reducing degradation. Especially, we formulated use cases for industrial settings in terms of arrangements of a data set. The results imply that a practitioner should care about better method continuously considering dataset availability and life cycle of an AI system because of a trade-off between accuracy and preventing degradation.

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Akihito Yoshii, Susumu Tokumoto, Fuyuki Ishikawa. 2022-05-14. Practical Insights of Repairing Model Problems on Image Classification. https://doi.org/10.1145/3522664.3528608

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