107 citations · 113 across the 5 of their papers we have counts for
7 papers
Informative Sample-Aware Proxy for Deep Metric Learning
Aoyu Li, Ikuro Sato, Kohta Ishikawa +2
Among various supervised deep metric learning methods proxy-based approaches have achieved high retrieval accuracies. Proxies, which are class-representative points in an embedding…
Adversarial Transformations for Semi-Supervised Learning
Teppei Suzuki, Ikuro Sato
We propose a Regularization framework based on Adversarial Transformations (RAT) for semi-supervised learning. RAT is designed to enhance robustness of the output distribution of c…
Breaking Inter-Layer Co-Adaptation by Classifier Anonymization
Ikuro Sato, Kohta Ishikawa, Guoqing Liu +1
This study addresses an issue of co-adaptation between a feature extractor and a classifier in a neural network. A naive joint optimization of a feature extractor and a classifier…
Generating Easy-to-Understand Referring Expressions for Target Identifications
Mikihiro Tanaka, Takayuki Itamochi, Kenichi Narioka +3
This paper addresses the generation of referring expressions that not only refer to objects correctly but also let humans find them quickly. As a target becomes relatively less sal…
Canonical and Compact Point Cloud Representation for Shape Classification
Kent Fujiwara, Ikuro Sato, Mitsuru Ambai +2
We present a novel compact point cloud representation that is inherently invariant to scale, coordinate change and point permutation. The key idea is to parametrize a distance fiel…
Binary-decomposed DCNN for accelerating computation and compressing model without retraining
Ryuji Kamiya, Takayoshi Yamashita, Mitsuru Ambai +3
Recent trends show recognition accuracy increasing even more profoundly. Inference process of Deep Convolutional Neural Networks (DCNN) has a large number of parameters, requires a…