617 citations · 714 across the 7 of their papers we have counts for
8 papers · 1 filter
How to cheat with metrics in single-image HDR reconstruction
Gabriel Eilertsen, Saghi Hajisharif, Param Hanji +3
Single-image high dynamic range (SI-HDR) reconstruction has recently emerged as a problem well-suited for deep learning methods. Each successive technique demonstrates an improveme…
Ensembles of GANs for synthetic training data generation
Gabriel Eilertsen, Apostolia Tsirikoglou, Claes Lundström +1
Insufficient training data is a major bottleneck for most deep learning practices, not least in medical imaging where data is difficult to collect and publicly available datasets a…
Classifying the classifier: dissecting the weight space of neural networks
Gabriel Eilertsen, Daniel Jönsson, Timo Ropinski +2
This paper presents an empirical study on the weights of neural networks, where we interpret each model as a point in a high-dimensional space -- the neural weight space. To explor…
A Closer Look at Domain Shift for Deep Learning in Histopathology
Karin Stacke, Gabriel Eilertsen, Jonas Unger +1
Domain shift is a significant problem in histopathology. There can be large differences in data characteristics of whole-slide images between medical centers and scanners, making g…
Single-frame Regularization for Temporally Stable CNNs
Gabriel Eilertsen, Rafał K. Mantiuk, Jonas Unger
Convolutional neural networks (CNNs) can model complicated non-linear relations between images. However, they are notoriously sensitive to small changes in the input. Most CNNs tra…
Synscapes: A Photorealistic Synthetic Dataset for Street Scene Parsing
Magnus Wrenninge, Jonas Unger
We introduce Synscapes -- a synthetic dataset for street scene parsing created using photorealistic rendering techniques, and show state-of-the-art results for training and validat…