1 citations · 2 across the 5 of their papers we have counts for
5 papers
Learning from AI: An Interactive Learning Method Using a DNN Model Incorporating Expert Knowledge as a Teacher
Kohei Hattori, Tsubasa Hirakawa, Takayoshi Yamashita +1
Visual explanation is an approach for visualizing the grounds of judgment by deep learning, and it is possible to visually interpret the grounds of a judgment for a certain input b…
PALF: Pre-Annotation and Camera-LiDAR Late Fusion for the Easy Annotation of Point Clouds
Yucheng Zhang, Masaki Fukuda, Yasunori Ishii +2
3D object detection has become indispensable in the field of autonomous driving. To date, gratifying breakthroughs have been recorded in 3D object detection research, attributed to…
Masking and Mixing Adversarial Training
Hiroki Adachi, Tsubasa Hirakawa, Takayoshi Yamashita +3
While convolutional neural networks (CNNs) have achieved excellent performances in various computer vision tasks, they often misclassify with malicious samples, a.k.a. adversarial…
Few-shot Adaptive Object Detection with Cross-Domain CutMix
Yuzuru Nakamura, Yasunori Ishii, Yuki Maruyama +1
In object detection, data amount and cost are a trade-off, and collecting a large amount of data in a specific domain is labor intensive. Therefore, existing large-scale datasets a…
Visual Explanation of Deep Q-Network for Robot Navigation by Fine-tuning Attention Branch
Yuya Maruyama, Hiroshi Fukui, Tsubasa Hirakawa +3
Robot navigation with deep reinforcement learning (RL) achieves higher performance and performs well under complex environment. Meanwhile, the interpretation of the decision-making…