Search Results for author:
Found 7 papers, 3 papers with code
Neural Density-Distance Fields
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.
RLTutor: Reinforcement Learning Based Adaptive Tutoring System by Modeling Virtual Student with Fewer Interactions
However, optimization using reinforcement learning requires a large number of interactions, and thus it cannot be applied directly to actual students.
ATRO: Adversarial Training with a Rejection Option
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
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.
What Do Adversarially Robust Models Look At?
In this paper, we address the open question: "What do adversarially robust models look at?"
Automatic Paper Summary Generation from Visual and Textual Information
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
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.
