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- 1Adversarial Robustness
- 1Adversarial Training
- 1Anomaly Detection
- 1Application logging (Computer science)
- 1Computer Vision
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Fall 2024
Nowadays systems logs are crucial for ensuring the reliability and security of modern computer systems. Effective log anomaly detection is essential for identifying potential threats and maintaining system integrity. Many existing unsupervised methods depend on additional abnormal data for...
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Fall 2022
Since 2013, Deep Neural Networks (DNNs) have caught up to a human-level performance at various benchmarks. Meanwhile, it is essential to ensure its safety and reliability. Recently an avenue of study questions the robustness of deep learning models and shows that adversarial samples with...
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Spring 2023
3D reconstruction of quadruped animals is a challenging problem, where key issues lie in their large shape variety and deformation within the same animal species as well as the lack of sufficient training data. In this thesis, we present two approaches toward this task. Our first approach is a...
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Fall 2024
This thesis delves into the advancements in visual anomaly detection (AD), a challenging task in identifying outliers in images such as defects and lesions, which is crucial in many applications including medical diagnosis and industrial manufacturing. This thesis addresses two main challenges:...