Reconstruction of Cloud Vertical Structure With a Generative Adversarial Network
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Over the past few years, there have been fundamental breakthroughs in core problems in machine learning, largely driven by advances in deep neural networks. The amount of annotated data drastically increased and supervised deep discriminative models exceed ...
In this Letter, we demonstrate how harmonic oscillator equations can be integrated in a neural network to improve the spectral response prediction for an optical system. We use the optical properties of a one-dimensional nanoslit array for a practical impl ...
Predictive scene parsing is a task of assigning pixel-level semantic labels to a future frame of a video. It has many applications in vision-based artificial intelligent systems, e.g., autonomous driving and robot navigation. Although previous work has sho ...
Purpose Regionalized life cycle impact assessment (LCIA) has rapidly developed in the past decade, though its widespread application, robustness, and validity still face multiple challenges. Under the umbrella of UNEP/SETAC Life Cycle Initiative, a dedicat ...
Thanks to the digital preservation of cultural heritage materials, multimedia tools (e.g., based on automatic visual processing) considerably ease the work of scholars in the humanities and help them to perform quantitative analysis of their data. In this ...
While several research studies have focused on analyzing human behavior and, in particular, emotional signals from visual data, the problem of synthesizing face video sequences with specific attributes (e.g. age, facial expressions) received much less atte ...
In learning from demonstrations, many generative models of trajectories make simplifying assumptions of independence. Correctness is sacrificed in the name of tractability and speed of the learning phase. The ignored dependencies, which are often the kinem ...
Radiomics has shown promising results in several medical studies, yet it suffers from a limited discrimination and informative capability as well as a high variation and correlation with the tomographic scanner types, pixel spacing, acquisition protocol, a ...
First-quantized deep neural network techniques are developed for analyzing strongly coupled fermionic systems on the lattice. Using a Slater-Jastrow-inspired ansatz which exploits deep residual networks with convolutional residual blocks, we approximately ...
The goal of Deep Domain Adaptation is to make it possible to use Deep Nets trained in one domain where there is enough annotated training data in another where there is little or none. Most current approaches have focused on learning feature representation ...