7 papers
VID-AD: A Dataset for Image-Level Logical Anomaly Detection under Vision-Induced Distraction
Hiroto Nakata, Yawen Zou, Shunsuke Sakai +5
Logical anomaly detection in industrial inspection remains challenging due to variations in visual appearance (e.g., background clutter, illumination shift, and blur), which often…
Few-shot Human Action Anomaly Detection via a Unified Contrastive Learning Framework
Koichiro Kamide, Shunsuke Sakai, Shun Maeda +2
Human Action Anomaly Detection (HAAD) aims to identify anomalous actions given only normal action data during training. Existing methods typically follow a one-model-per-category p…
Contrastive Learning-Enhanced Trajectory Matching for Small-Scale Dataset Distillation
Wenmin Li, Shunsuke Sakai, Tatsuhito Hasegawa
Deploying machine learning models in resource-constrained environments, such as edge devices or rapid prototyping scenarios, increasingly demands distillation of large datasets int…
Analytical Softmax Temperature Setting from Feature Dimensions for Model- and Domain-Robust Classification
Tatsuhito Hasegawa, Shunsuke Sakai
In deep learning-based classification tasks, the softmax function's temperature parameter critically influences the output distribution and overall performance. This study pres…
Noisy Deep Ensemble: Accelerating Deep Ensemble Learning via Noise Injection
Shunsuke Sakai, Shunsuke Tsuge, Tatsuhito Hasegawa
Neural network ensembles is a simple yet effective approach for enhancing generalization capabilities. The most common method involves independently training multiple neural networ…
InvAD: Inversion-based Reconstruction-Free Anomaly Detection with Diffusion Models
Shunsuke Sakai, Xiangteng He, Chunzhi Gu +2
Despite the remarkable success, recent reconstruction-based anomaly detection (AD) methods via diffusion modeling still involve fine-grained noise-strength tuning and computational…