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 …

Deep neural networks have improved image classification dramatically over the past
decade, but have done so by focusing on performance measures that treat all classes other …

Deep neural networks trained for classification have been found to learn powerful image
representations, which are also often used for other tasks such as comparing images wrt …

The CIFAR-10 and CIFAR-100 datasets are two of the most heavily benchmarked datasets
in computer vision and are often used to evaluate novel methods and model architectures in …

Camera traps mounted on highway bridges capture millions of images that allow
investigating animal populations and their behavior. As the manual analysis of such an …

Content-based Image Retrieval (CBIR) ist ein Verfahren zum Auffinden von Bildern in
großen Datenbanken wie z. B. dem Internet anhand ihres Inhalts. Ausgehend von einem …

One of the most important challenges in the Machine and Deep Learning areas today is to
build good models using small datasets, because sometimes it is not possible to have large …

In this work, we propose a novel method for weakly supervised histopathology whole slide
image (WSI) classification for addressing the breast cancer detection task. Some of the …

One of the most important challenges in the Machine and Deep Learning areas today is to
build good models using small datasets, because sometimes it is not possible to have large …