4 papers
Multiple Domain Generalization Using Category Information Independent of Domain Differences
Reiji Saito, Kazuhiro Hotta
Domain generalization is a technique aimed at enabling models to maintain high accuracy when applied to new environments or datasets (unseen domains) that differ from the datasets…
Accuracy Improvement of Semi-Supervised Segmentation Using Supervised ClassMix and Sup-Unsup Feature Discriminator
Takahiro Mano, Reiji Saito, Kazuhiro Hotta
In semantic segmentation, the creation of pixel-level labels for training data incurs significant costs. To address this problem, semi-supervised learning, which utilizes a small n…
Novel Anomaly Detection Scenarios and Evaluation Metrics to Address the Ambiguity in the Definition of Normal Samples
Reiji Saito, Satoshi Kamiya, Kazuhiro Hotta
In conventional anomaly detection, training data consist of only normal samples. However, in real-world scenarios, the definition of a normal sample is often ambiguous. For example…
Domain Generalization through Attenuation of Domain-Specific Information
Reiji Saito, Kazuhiro Hotta
In this paper, we propose a new evaluation metric called Domain Independence (DI) and Attenuation of Domain-Specific Information (ADSI) which is specifically designed for domain-ge…