62 citations · 77 across the 10 of their papers we have counts for
10 papers
Enlarged Large Margin Loss for Imbalanced Classification
Sota Kato, Kazuhiro Hotta
We propose a novel loss function for imbalanced classification. LDAM loss, which minimizes a margin-based generalization bound, is widely utilized for class-imbalanced image classi…
Reconstructed Student-Teacher and Discriminative Networks for Anomaly Detection
Shinji Yamada, Satoshi Kamiya, Kazuhiro Hotta
Anomaly detection is an important problem in computer vision; however, the scarcity of anomalous samples makes this task difficult. Thus, recent anomaly detection methods have used…
Adversarial Mutual Leakage Network for Cell Image Segmentation
Hiroki Tsuda, Kazuhiro Hotta
We propose three segmentation methods using GAN and information leakage between generator and discriminator. First, we propose an Adversarial Training Attention Module (ATA-Module)…
Automatic Preprocessing and Ensemble Learning for Low Quality Cell Image Segmentation
Sota Kato, Kazuhiro Hotta
We propose an automatic preprocessing and ensemble learning for segmentation of cell images with low quality. It is difficult to capture cells with strong light. Therefore, the mic…
MSE Loss with Outlying Label for Imbalanced Classification
Sota Kato, Kazuhiro Hotta
In this paper, we propose mean squared error (MSE) loss with outlying label for class imbalanced classification. Cross entropy (CE) loss, which is widely used for image recognition…
Cell image segmentation by Feature Random Enhancement Module
Takamasa Ando, Kazuhiro Hotta
It is important to extract good features using an encoder to realize semantic segmentation with high accuracy. Although loss function is optimized in training deep neural network,…