Visual design is associated with the use of some basic design elements and principles.
However, there are two drawbacks of the approach: most of the edges in the graph are assigned randomly and the GCN is trained independently from the segmentation network.
A probabilistic classifier with reliable predictive uncertainties i) fits successfully to the target domain data, ii) provides calibrated class probabilities in difficult regions of the target domain (e. g.\ class overlap), and iii) accurately identifies queries coming out of the target domain and reject them.
Learning new representations of 3D point clouds is an active research area in 3D vision, as the order-invariant point cloud structure still presents challenges to the design of neural network architectures.
Lastly, we show the contribution of the self-supervision tasks to the GAN training on the loss landscape and present that the effects of these tasks may not be cooperative to the adversarial training in some settings.
We propose a new approach for the problem of relative depth estimation from a single image.
To this end, we have introduced a listwise ranking loss borrowed from ranking literature, weighted ListMLE, to the relative depth estimation problem.
Deep neural network training without pre-trained weights and few data is shown to need more training iterations.
Ranked #1 on Semantic Segmentation on Cityscapes VIPriors subset
Convolutional Neural Networks (CNN)-based approaches have shown promising results in pansharpening of satellite images in recent years.
Generative Adversarial Networks (GANs) triggered an increased interest in problem of image generation due to their improved output image quality and versatility for expansion towards new methods.
This compendium gathers all the accepted extended abstracts from the Second International Conference on Medical Imaging with Deep Learning (MIDL 2019), held in London, UK, 8-10 July 2019.
To that end, we incorporate steerable filter responses of the generated and reference images inside a Huber function loss term.
Area of image inpainting over relatively large missing regions recently advanced substantially through adaptation of dedicated deep neural networks.