Recent research has found that many families of machine learning models are vulnerable to
adversarial examples: inputs that are specifically designed to cause the target model to …

The topical domain generalization (DG) problem asks trained models to perform well on an
unseen target domain with different data statistics from the source training domains. In …

Recent work has shown that convolutional networks can be substantially deeper, more
accurate, and efficient to train if they contain shorter connections between layers close to the …

Deep neural networks (DNNs) are powerful nonlinear architectures that are known to be
robust to random perturbations of the input. However, these models are vulnerable to …

This paper introduces a deep-learning approach to photographic style transfer that handles
a large variety of image content while faithfully transferring the reference style. Our approach …

Clothing-change person re-identification (CC Re-ID) aims to match the same person who
changes clothes across cameras. Current methods are usually limited by the insufficient …

In view of the lack of feature complementarity between the feature layers of Single Shot
MultiBox Detector (SSD) and the weak detection ability of SSD for small objects, we propose …

Abstract We propose Deep Feature Interpolation (DFI), a new data-driven baseline for
automatic high-resolution image transformation. As the name suggests, DFI relies only on …

Traditional face editing methods often require a number of sophisticated and task specific
algorithms to be applied one after the other---a process that is tedious, fragile, and …

This book provides an introduction to the mathematical and algorithmic foundations of data
science, including machine learning, high-dimensional geometry, and analysis of large …