With the broader and highly successful usage of machine learning (ML) in industry and the
sciences, there has been a growing demand for explainable artificial intelligence (XAI) …

Image classification with small datasets has been an active research area in the recent past.
However, as research in this scope is still in its infancy, two key ingredients are missing for …

Two things seem to be indisputable in the contemporary deep learning discourse: 1. The
categorical cross-entropy loss after softmax activation is the method of choice for …

Over the past few years, a significant progress has been made in deep convolutional neural
networks (CNNs)-based image recognition. This is mainly due to the strong ability of such …

Power Normalizations (PN) are useful non-linear operators which tackle feature imbalances
in classification problems. We study PNs in the deep learning setup via a novel PN layer …

Explainable AI aims to overcome the black-box nature of complex ML models like neural
networks by generating explanations for their predictions. Explanations often take the form of …

Many learning algorithms such as kernel machines, nearest neighbors, clustering, or
anomaly detection, are based on distances or similarities. Before similarities are used for …

Fine-grained image recognition is very challenging due to the difficulty of capturing both
semantic global features and discriminative local features. Meanwhile, these two features …

Abstract Human Action Recognition (HAR) has achieved a remarkable milestone in the field
of computer vision. Apart from its varied applications in human–computer interactions …

Fine-grained visual categorization is a classification task for distinguishing categories with
high intra-class and small inter-class variance. While global approaches aim at using the …