2 citations · 2 across the 5 of their papers we have counts for
5 papers
Denoising Cosine Similarity: A Theory-Driven Approach for Efficient Representation Learning
Takumi Nakagawa, Yutaro Sanada, Hiroki Waida +5
Representation learning has been increasing its impact on the research and practice of machine learning, since it enables to learn representations that can apply to various downstr…
Towards Understanding the Mechanism of Contrastive Learning via Similarity Structure: A Theoretical Analysis
Hiroki Waida, Yuichiro Wada, Léo Andéol +3
Contrastive learning is an efficient approach to self-supervised representation learning. Although recent studies have made progress in the theoretical understanding of contrastive…
Deep Clustering with a Constraint for Topological Invariance based on Symmetric InfoNCE
Yuhui Zhang, Yuichiro Wada, Hiroki Waida +3
We consider the scenario of deep clustering, in which the available prior knowledge is limited. In this scenario, few existing state-of-the-art deep clustering methods can perform…
DGP-Net: Dense Graph Prototype Network for Few-Shot SAR Target Recognition
Xiangyu Zhou, Qianru Wei, Yuhui Zhang
The inevitable feature deviation of synthetic aperture radar (SAR) image due to the special imaging principle (depression angle variation) leads to poor recognition accuracy, espec…
Irregular Convolutional Auto-Encoder on Point Clouds
Zhang Yuhui, Greg Gutmann, Konagaya Akihiko
We proposed a novel graph convolutional neural network that could construct a coarse, sparse latent point cloud from a dense, raw point cloud. With a novel non-isotropic convolutio…