IMG2DSM: Height Simulation From Single Imagery Using Conditional Generative Adversarial Net
نویسندگان
چکیده
منابع مشابه
Conditional Generative Adversarial Nets
Generative Adversarial Nets [8] were recently introduced as a novel way to train generative models. In this work we introduce the conditional version of generative adversarial nets, which can be constructed by simply feeding the data, y, we wish to condition on to both the generator and discriminator. We show that this model can generate MNIST digits conditioned on class labels. We also illustr...
متن کاملEnhancing Underwater Imagery using Generative Adversarial Networks
Autonomous underwater vehicles (AUVs) rely on a variety of sensors – acoustic, inertial and visual – for intelligent decision making. Due to its non-intrusive, passive nature, and high information content, vision is an attractive sensing modality, particularly at shallower depths. However, factors such as light refraction and absorption, suspended particles in the water, and color distortion af...
متن کاملContext-conditional Generative Adversarial Networks
We introduce a simple semi-supervised learning approach for images based on in-painting using an adversarial loss. Images with random patches removed are presented to a generator whose task is to fill in the hole, based on the surrounding pixels. The in-painted images are then presented to a discriminator network that judges if they are real (unaltered training images) or not. This task acts as...
متن کاملTowards Recovery of Conditional Vectors from Conditional Generative Adversarial Networks
A conditional Generative Adversarial Network allows for generating samples conditioned on certain external information. Being able to recover latent and conditional vectors from a conditional GAN can be potentially valuable in various applications, ranging from image manipulation for entertaining purposes to diagnosis of the neural networks for security purposes. In this work, we show that it i...
متن کاملBidirectional Conditional Generative Adversarial Networks
Conditional Generative Adversarial Networks (cGANs) are generative models that can produce data samples (x) conditioned on both latent variables (z) and known auxiliary information (c). We propose the Bidirectional cGAN (BiCoGAN), which effectively disentangles z and c in the generation process and provides an encoder that learns inverse mappings from x to both z and c, trained jointly with the...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Geoscience and Remote Sensing Letters
سال: 2018
ISSN: 1545-598X,1558-0571
DOI: 10.1109/lgrs.2018.2806945