Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
Results
Showing 1 to 11 of 11 for “"Out-of-distribution Detection"”.
-
Statistical Methods for Out-of-distribution Detection
For a network trained on in-distribution (ID) samples, test samples could be out-of-distribution (OOD) that are drawn from distributions different from that of ID samples. Accordingly, OOD detection aims to identify OOD samples in test phases. The main challenge lies in that a network could provide …
-
Regularization, Uncertainty Estimation and Out of Distribution Detection in Convolutional Neural Networks
Classification is an important task in the field of machine learning and when classifiers are trained on images, a variety of problems can surface during inference. 1) Recent trends of using convolutional neural networks (CNNs) for various machine learning tasks has borne many successes and CNNs …
-
Improved Out-of-Distribution Detection Using Segmented Images and Prompt-Only Text Reasoning
The problem of Out-of-Distribution (OOD) detection has been thoroughly researched but continues to underperform in "near-OOD" settings, where OOD data may be very similar or inseparable from in-distribution (ID) data. The problem is that current state-of-the-art OOD detection methods fail to learn …
-
SAFEGUARDING AI SYSTEMS AGAINST UNEXPECTED INPUTS
… achieved remarkable success across a broad range of applications. However, perturbations such as natural image corruptions or crafted malicious queries, can cause significant performance degradation. This poses severe risks in safety-critical applications, such as autonomous driving and clinical …
-
Certifying robustness in inference and learning problems
There is a rich literature of algorithms for inference, prediction, and decision-making problems when the underlying distributions governing the data are known and well-modeled. The research from the past few decades has provided powerful learning algorithms when such distributions cannot be easily …
-
Towards Learning the Geometry of Data: From Diffusion Models to Riemannian Geometry
… the way for learning the intrinsic geometry of data manifolds. In chapter 3, we introduce CAFLOW, a conditional normalising flow that improves image-to-image translation by hierarchically modelling image distributions across scales. In chapter 4, we introduce non-uniform diffusion models, …
-
Epistemic deep learning : enabling machine learning models to ‘know when they do not know’
… and unreliable predictions when models encounter out-of-distribution data, adversarial perturbations, or naturally fluctuating environments. This thesis, titled Epistemic Deep Learning: Enabling Machine Learning Models to ‘Know When They Do Not Know’, addresses these critical challenges by …
-
Engineering-driven Machine Learning Methods for System Intelligence
… learning methods, and the industrial internet of things (IIoT). The development of sensing technology provides large amounts and various types of data (e.g., profile, image, point cloud, etc.) to describe each stage of a manufacturing process. The machine learning methods have the advantages of …
-
Advances in Probabilistic Deep Learning and Their Applications
… aims to unify the two, with the potential to offer compelling theoretical properties and practical functional benefits across a variety of problems. This thesis provides contributions to the methodology and application of probabilistic deep learning. In particular, we develop new methods to …
-
Enhancing Robustness and Interpretability in Computer Vision AI
L'abstract è presente nell'allegato / the abstract is in the attachment