| Budget Amount *help |
¥44,460,000 (Direct Cost: ¥34,200,000、Indirect Cost: ¥10,260,000)
Fiscal Year 2022: ¥10,400,000 (Direct Cost: ¥8,000,000、Indirect Cost: ¥2,400,000)
Fiscal Year 2021: ¥14,950,000 (Direct Cost: ¥11,500,000、Indirect Cost: ¥3,450,000)
Fiscal Year 2020: ¥13,650,000 (Direct Cost: ¥10,500,000、Indirect Cost: ¥3,150,000)
Fiscal Year 2019: ¥5,460,000 (Direct Cost: ¥4,200,000、Indirect Cost: ¥1,260,000)
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| Outline of Final Research Achievements |
Recent successes in deep learning have dramatically improved the accuracy of image recognition but achieving high recognition performance requires a huge amount of supervised data. Generating high-quality supervised data requires a lot of human effort and cost, which is a major problem in machine learning. In this study, we developed a method for learning highly accurate image recognition models with only a small amount of supervised data. Specifically, we developed a methodology to maximize the discriminative power of deep learning by making the most of limited supervised data, a domain adaptation method that enables knowledge transfer between different domains, and active information acquisition for efficient generation of supervised data.
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