11 citations · 15 across the 5 of their papers we have counts for
5 papers
Three approaches to facilitate DNN generalization to objects in out-of-distribution orientations and illuminations
Akira Sakai, Taro Sunagawa, Spandan Madan +7
The training data distribution is often biased towards objects in certain orientations and illumination conditions. While humans have a remarkable capability of recognizing objects…
Annotation Cost Reduction of Stream-based Active Learning by Automated Weak Labeling using a Robot Arm
Kanata Suzuki, Taro Sunagawa, Tomotake Sasaki +1
Stream-based active learning (AL) is an efficient training data collection method, and it is used to reduce human annotation cost required in machine learning. However, it is diffi…
Selective Forgetting of Deep Networks at a Finer Level than Samples
Tomohiro Hayase, Suguru Yasutomi, Takashi Katoh
Selective forgetting or removing information from deep neural networks (DNNs) is essential for continual learning and is challenging in controlling the DNNs. Such forgetting is cru…
Multi Instance Learning For Unbalanced Data
Mark Kozdoba, Edward Moroshko, Lior Shani +4
In the context of Multi Instance Learning, we analyze the Single Instance (SI) learning objective. We show that when the data is unbalanced and the family of classifiers is suffici…
In-Place Initializable Arrays
Takashi Katoh, Keisuke Goto
An initializable array is an array that supports the read and write operations for any element and the initialization of the entire array. This paper proposes a simple in-place alg…