| Budget Amount *help |
¥4,420,000 (Direct Cost: ¥3,400,000、Indirect Cost: ¥1,020,000)
Fiscal Year 2019: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2018: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2017: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
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| Outline of Final Research Achievements |
In this study, we proposed a new compact deep neural network (DNN) architecture based on lifting complex wavelets. This model is represented with fewer parameters than existing CNN models, which is expected to save memory and speed up computation. In practice, there are still some challenges in speeding up the training time, such as the need to implement the complex lifting wavelet transforms on a GPU.
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