Naturalness-Preserving Image Tone Enhancement Using Generative Adversarial Networks (Supplementary Material)

For the generation of synthetic ALS-predicted AGB patches from SEN1A data, we train nine different cGAN architectures (combining the three ResNet networks and the three

Initially, we show that neural networks are able to approximate the KL-divergence between two distributions, and go on to use this with a generative network to learn the posterior

Supported by the results where the ForGAN and MC dropout were able to produce both more accurate point forecasts and prediction intervals, the decreasing trend might be a

By training a “reverse" model that predicts images with text given the de-identified image as input.. We did make a small attempt at this when when working with the

We consider the light field synthesis problem equivalent to image super-resolution, and solve it by using the improved Wasserstein Generative Adversarial Network with gradient

We consider the light field synthesis problem equivalent to image super-resolution, and solve it by using the improved Wasserstein Generative Adversarial Network with gradient

Figure 1: Given an input exemplar and a target portrait photo, we can generate stylized output with comparable or superior visual quality as compared to several state-of-the-art

radar (e) IL Gauss-pos (f) IL Gauss-radar Figure 4: Example paths taken by the USV: (a) Ex- ample of a path taken by an expert (b) Successful episode of the IL setup with