107 citations · 113 across the 7 of their papers we have counts for
6 papers · 1 filter
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…
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…
APAC: Augmented PAttern Classification with Neural Networks
Ikuro Sato, Hiroki Nishimura, Kensuke Yokoi
Deep neural networks have been exhibiting splendid accuracies in many of visual pattern classification problems. Many of the state-of-the-art methods employ a technique known as da…