1 citations · 1 across the 5 of their papers we have counts for
5 papers
DuoAD: Leveraging [CLS] Dual Characteristics for Training-Free Few-Shot Anomaly Detection
Jyun-Ze Tang, Po-Han Huang, Ming-Ching Chang +2
Vision foundation models have enabled strong training-free anomaly detection (AD). However, most existing approaches rely primarily on independent local patch features, leaving the…
Continual Learning with Support Boundary Experience Blending
Chih-Fan Hsu, Ming-Ching Chang, Wei-Chao Chen
Continual learning (CL) seeks to mitigate catastrophic forgetting when models are trained with sequential tasks. A common approach, experience replay (ER), stores past exemplars bu…
Learning with Instance-Dependent Noisy Labels by Anchor Hallucination and Hard Sample Label Correction
Po-Hsuan Huang, Chia-Ching Lin, Chih-Fan Hsu +2
Learning from noisy-labeled data is crucial for real-world applications. Traditional Noisy-Label Learning (NLL) methods categorize training data into clean and noisy sets based on…
A Comprehensive Review of Machine Learning Advances on Data Change: A Cross-Field Perspective
Jeng-Lin Li, Chih-Fan Hsu, Ming-Ching Chang +1
Recent artificial intelligence (AI) technologies show remarkable evolution in various academic fields and industries. However, in the real world, dynamic data lead to principal cha…
Mitigating Data Absence in Federated Learning Using Privacy-Controllable Data Digests
Chih-Fan Hsu, Ming-Ching Chang, Wei-Chao Chen
The absence of training data and their distribution changes in federated learning (FL) can significantly undermine model performance, especially in cross-silo scenarios. To address…