|
|
|
|
|
Shumin Lai, Longjun Huang, Ping Li, Zhenzhen Luo, Jianzhong Wang and Yugen Yi
In this paper, we present a novel unsupervised feature selection method termed robust matrix factorization with robust adaptive structure learning (RMFRASL), which can select discriminative features from a large amount of multimedia data to improve the p...
ver más
|
|
|
|
|
|
|
|
|
Mei Wang, Qingshan Mei, Xiyu Song, Xin Liu, Ruixiang Kan, Fangzhi Yao, Junhan Xiong and Hongbing Qiu
Unsupervised anomalous sound detection by machines holds significant importance within the realm of industrial automation. Currently, the task of machine-based anomalous sound detection in complex industrial settings is faced with issues such as the chal...
ver más
|
|
|
|
|
|
|
|
|
Dawei Luo, Heng Zhou, Joonsoo Bae and Bom Yun
Reliability and robustness are fundamental requisites for the successful integration of deep-learning models into real-world applications. Deployed models must exhibit an awareness of their limitations, necessitating the ability to discern out-of-distrib...
ver más
|
|
|
|
|
|
|
|
|
Raluca Chitic, Ali Osman Topal and Franck Leprévost
Recently, convolutional neural networks (CNNs) have become the main drivers in many image recognition applications. However, they are vulnerable to adversarial attacks, which can lead to disastrous consequences. This paper introduces ShuffleDetect as a n...
ver más
|
|
|
|
|
|
|
|
|
Zhaobin Duan, Xidan Cao, Fangyu Hu, Peng Wang, Xi Chen and Lei Dong
Traditional methods are unable to effectively assess the health status of engine bleed air systems. To address the limitation, this paper proposes a methodology for constructing health indicators using multi-level feature extraction. First, this approach...
ver más
|
|
|
|