Search Results for author: Yoshihiro Fukuhara

Found 7 papers, 3 papers with code

Neural Density-Distance Fields

1 code implementation29 Jul 2022 Itsuki Ueda, Yoshihiro Fukuhara, Hirokatsu Kataoka, Hiroaki Aizawa, Hidehiko Shishido, Itaru Kitahara

However, it is difficult to achieve high localization performance by only density fields-based methods such as Neural Radiance Field (NeRF) since they do not provide density gradient in most empty regions.

Novel View Synthesis Visual Localization

RLTutor: Reinforcement Learning Based Adaptive Tutoring System by Modeling Virtual Student with Fewer Interactions

1 code implementation31 Jul 2021 Yoshiki Kubotani, Yoshihiro Fukuhara, Shigeo Morishima

However, optimization using reinforcement learning requires a large number of interactions, and thus it cannot be applied directly to actual students.

Decision Making reinforcement-learning

ATRO: Adversarial Training with a Rejection Option

no code implementations24 Oct 2020 Masahiro Kato, Zhenghang Cui, Yoshihiro Fukuhara

In this paper, in order to acquire a more reliable classifier against adversarial attacks, we propose the method of Adversarial Training with a Rejection Option (ATRO).

Learning with Protection: Rejection of Suspicious Samples under Adversarial Environment

no code implementations25 Sep 2019 Masahiro Kato, Yoshihiro Fukuhara, Hirokatsu Kataoka, Shigeo Morishima

Our main idea is to apply a framework of learning with rejection and adversarial examples to assist in the decision making for such suspicious samples.

BIG-bench Machine Learning Decision Making +2

What Do Adversarially Robust Models Look At?

1 code implementation19 May 2019 Takahiro Itazuri, Yoshihiro Fukuhara, Hirokatsu Kataoka, Shigeo Morishima

In this paper, we address the open question: "What do adversarially robust models look at?"

Adversarial Robustness

Automatic Paper Summary Generation from Visual and Textual Information

no code implementations16 Nov 2018 Shintaro Yamamoto, Yoshihiro Fukuhara, Ryota Suzuki, Shigeo Morishima, Hirokatsu Kataoka

Due to the recent boom in artificial intelligence (AI) research, including computer vision (CV), it has become impossible for researchers in these fields to keep up with the exponentially increasing number of manuscripts.

Understanding Fake Faces

no code implementations22 Sep 2018 Ryota Natsume, Kazuki Inoue, Yoshihiro Fukuhara, Shintaro Yamamoto, Shigeo Morishima, Hirokatsu Kataoka

Face recognition research is one of the most active topics in computer vision (CV), and deep neural networks (DNN) are now filling the gap between human-level and computer-driven performance levels in face verification algorithms.

Face Recognition Face Verification

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