27 citations · 70 across the 11 of their papers we have counts for
11 papers
Fixed-Weight Difference Target Propagation
Tatsukichi Shibuya, Nakamasa Inoue, Rei Kawakami +1
Target Propagation (TP) is a biologically more plausible algorithm than the error backpropagation (BP) to train deep networks, and improving practicality of TP is an open issue. TP…
Parameter Efficient Transfer Learning for Various Speech Processing Tasks
Shinta Otake, Rei Kawakami, Nakamasa Inoue
Fine-tuning of self-supervised models is a powerful transfer learning method in a variety of fields, including speech processing, since it can utilize generic feature representatio…
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…
PoF: Post-Training of Feature Extractor for Improving Generalization
Ikuro Sato, Ryota Yamada, Masayuki Tanaka +2
It has been intensively investigated that the local shape, especially flatness, of the loss landscape near a minimum plays an important role for generalization of deep models. We d…
Feature Space Particle Inference for Neural Network Ensembles
Shingo Yashima, Teppei Suzuki, Kohta Ishikawa +2
Ensembles of deep neural networks demonstrate improved performance over single models. For enhancing the diversity of ensemble members while keeping their performance, particle-bas…
Finding a Needle in a Haystack: Tiny Flying Object Detection in 4K Videos using a Joint Detection-and-Tracking Approach
Ryota Yoshihashi, Rei Kawakami, Shaodi You +3
Detecting tiny objects in a high-resolution video is challenging because the visual information is little and unreliable. Specifically, the challenge includes very low resolution o…
Hybrid Loss for Learning Single-Image-based HDR Reconstruction
Kenta Moriwaki, Ryota Yoshihashi, Rei Kawakami +2
This paper tackles high-dynamic-range (HDR) image reconstruction given only a single low-dynamic-range (LDR) image as input. While the existing methods focus on minimizing the mean…
Classification-Reconstruction Learning for Open-Set Recognition
Ryota Yoshihashi, Wen Shao, Rei Kawakami +3
Open-set classification is a problem of handling `unknown' classes that are not contained in the training dataset, whereas traditional classifiers assume that only known classes ap…
Cross-connected Networks for Multi-task Learning of Detection and Segmentation
Seiichiro Fukuda, Ryota Yoshihashi, Rei Kawakami +3
Multi-task learning improves generalization performance by sharing knowledge among related tasks. Existing models are for task combinations annotated on the same dataset, while the…
Differentiating Objects by Motion: Joint Detection and Tracking of Small Flying Objects
Ryota Yoshihashi, Tu Tuan Trinh, Rei Kawakami +3
While generic object detection has achieved large improvements with rich feature hierarchies from deep nets, detecting small objects with poor visual cues remains challenging. Moti…
Waterdrop Stereo
Shaodi You, Robby T. Tan, Rei Kawakami +2
This paper introduces depth estimation from water drops. The key idea is that a single water drop adhered to window glass is totally transparent and convex, and thus optically acts…