Detecting Planar Surface Using a Light-Field Camera with Application to Distinguishing Real Scenes From Printed Photos

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In this thesis, we study two closely related directions: robustness and generalization in modern deep learning. Deep learning models based on empirical risk minimization are known to be often non-robust to small, worst-case perturbations known as adversari ...

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Detecting Planar Surface Using a Light-Field Camera with Application to Distinguishing Real Scenes From Printed Photos

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Understanding generalization and robustness in modern deep learning

Rigidity-Aware Detection for 6D Object Pose Estimation

Towards learning-based denoising of light fields

Seeing Photons in Color

SoDaCam: Software-defined Cameras via Single-Photon Imaging

Self-supervised Dense Representation Learning for Live-Cell Microscopy with Time Arrow Prediction

Ex uno plures: how to construct high-speed movies of collapsing cavitation bubbles from a single image

Performance investigation of cameras using HDR sensors for daylight glare evaluations

Data-driven approaches for non-invasive cuffless blood pressure monitoring

Robust Outlier Rejection for 3D Registration with Variational Bayes

Cross-resolution Face Recognition via Identity-Preserving Network and Knowledge Distillation

Price Discrimination with Robust Beliefs

Detecting Anomalies and Obstacles in Road Scenes

Self-Supervised Learning for Patient Stratification and Survival Analysis in Computational Pathology: An Application to Colorectal Cancer

Image-based geometric digital twinning for stone masonry elements

Learning Disentangled Behaviour Patterns for Wearable-based Human Activity Recognition

Metric learning for kernel ridge regression: assessment of molecular similarity

Indexing Protected Deep Face Templates by Frequent Binary Patterns

System Support for Robust Distributed Learning

Improving the Training of Compact Neural Networks for Visual Recognition