Convolutional neural networks raised the bar for machine learning and artificial intelligence
applications, mainly due to the abundance of data and computations. However, there is not …

Detecting objects with a small representation in images is a challenging task, especially
when the style of the images is very different from recent photos, which is the case for …

Outlier detection is one of the main fields in machine learning and it has been growing
rapidly due to its wide range of applications. In the last few years, deep learning-based …

The biggest fear when deploying machine learning models to the real world is their ability to
handle the new data. This problem is significant especially in medicine, where models …

Image classification models have improved drastically due to neural networks. But as a
direct consequence of being trained on a specific dataset, neural networks tend to be biased …

Landmark detection is an essential step in the diagnosis of bone pathologies and pelvis
morphometry. Hence, we propose a Deep Learning based method for automatic landmark …

Abstract Domain generalization is a sub-field of transfer learning that aims at bridging the
gap between two different domains in the absence of any knowledge about the target …

Requirements elicitation is an important phase of any software project: the errors in
requirements are more expensive to fix than the errors introduced at later stages of software …

This work presents an analysis of the effectiveness of image augmentations for the problem
of face recognition from limited data. We considered basic manipulations, generative …

Local outlier detection is a critical task in data mining, which identifies outliers by estimating
the anomaly score for each data point. The traditional density-based methods assign large k …