482 citations · 589 across the 6 of their papers we have counts for
6 papers
ChartPointFlow for Topology-Aware 3D Point Cloud Generation
Takumi Kimura, Takashi Matsubara, Kuniaki Uehara
A point cloud serves as a representation of the surface of a three-dimensional (3D) shape. Deep generative models have been adapted to model their variations typically using a map…
Exploring Uncertainty Measures for Image-Caption Embedding-and-Retrieval Task
Kenta Hama, Takashi Matsubara, Kuniaki Uehara +1
With the wide development of black-box machine learning algorithms, particularly deep neural network (DNN), the practical demand for the reliability assessment is rapidly rising. O…
Data Augmentation using Random Image Cropping and Patching for Deep CNNs
Ryo Takahashi, Takashi Matsubara, Kuniaki Uehara
Deep convolutional neural networks (CNNs) have achieved remarkable results in image processing tasks. However, their high expression ability risks overfitting. Consequently, data a…
Deep Generative Model using Unregularized Score for Anomaly Detection with Heterogeneous Complexity
Takashi Matsubara, Kenta Hama, Ryosuke Tachibana +1
Accurate and automated detection of anomalous samples in a natural image dataset can be accomplished with a probabilistic model for end-to-end modeling of images. Such images have…
Deep Neural Generative Model of Functional MRI Images for Psychiatric Disorder Diagnosis
Takashi Matsubara, Tetsuo Tashiro, Kuniaki Uehara
Accurate diagnosis of psychiatric disorders plays a critical role in improving the quality of life for patients and potentially supports the development of new treatments. Many stu…
A Novel Weight-Shared Multi-Stage CNN for Scale Robustness
Ryo Takahashi, Takashi Matsubara, Kuniaki Uehara
Convolutional neural networks (CNNs) have demonstrated remarkable results in image classification for benchmark tasks and practical applications. The CNNs with deeper architectures…