… to use U-net architecture for semantic segmentation of landslide … Visual analysis show surface
features which are comparable … detection CNN for change detection; post-processing with …

… revolutionize the field of visual representation learning in a self-… flexible use of various deep
architectures as network backbones, … Qualitative and quantitative change detection results of …

… recognition FER via deep learning. We underline on these contributions treated, the architecture
… and databases, for example the fusion of audio and visual studied by Zhang et al. [37] …

… for vision. Having said that, a lot of what’s happened over the past decade is that deep learning
… With the maturity of today’s neural-network architectures, I think for a lot of the practical …

… deep learning and self-driving cars through a comprehensive survey. We begin with an
introduction to self-driving cars, deep learning, and computer vision … deep learning architectures …

… Early solutions to computer vision tasks depended on traditional machine learning methods,
… , and the ResNet architecture is easier to optimize than other deep networks. Inception-…

… propose a novel deep learning workflow that couples hierarchical vision transformers with
… of deep learning architectures in using generative adversarial networks (GAN) and Vision …

… Improvements in network architectures often transfer significant performance … visual
representations. Next, we present five deep learning architectures widely used in computer vision …

… system can address tasks such as food grading, detecting locations of defective spots or …
on the traditional machine learning and deep learning methods, as well as the machine vision …

… a type of brain disease which causes visual, sensory, and motor … involves conventional
machine learning and deep learning (… DL architecture is obtained to detect the MS automatically. …