Search Results for author:
Found 10 papers, 1 papers with code
PolyWorld: Polygonal Building Extraction with Graph Neural Networks in Satellite Images
While most state-of-the-art instance segmentation methods produce binary segmentation masks, geographic and cartographic applications typically require precise vector polygons of extracted objects instead of rasterized output.
Re-Training StyleGAN -- A First Step Towards Building Large, Scalable Synthetic Facial Datasets
The role of this tool in building large, scalable datasets of synthetic facial data is also discussed.
Deep Learning Based Computed Tomography Whys and Wherefores
This is an article about the Computed Tomography (CT) and how Deep Learning influences CT reconstruction pipeline, especially in low dose scenarios.
Deep Neural Network and Data Augmentation Methodology for off-axis iris segmentation in wearable headsets
A data augmentation methodology is presented and applied to generate a large dataset of off-axis iris regions and train a low-complexity deep neural network.
Versatile Auxiliary Classifier with Generative Adversarial Network (VAC+GAN), Multi Class Scenarios
Conditional generators learn the data distribution for each class in a multi-class scenario and generate samples for a specific class given the right input from the latent space.
Versatile Auxiliary Regressor with Generative Adversarial network (VAR+GAN)
Conditional generators are one of the successful implementations of such models wherein the created samples are constrained to a specific class.
Versatile Auxiliary Classifier with Generative Adversarial Network (VAC+GAN)
One of the most interesting challenges in Artificial Intelligence is to train conditional generators which are able to provide labeled adversarial samples drawn from a specific distribution.
Face Synthesis with Landmark Points from Generative Adversarial Networks and Inverse Latent Space Mapping
There have been a tremendous amount of attempts to detect these points from facial images however, there has never been an attempt to synthesize a random face and generate its corresponding facial landmarks.
Image and Video Processing
An End to End Deep Neural Network for Iris Segmentation in Unconstraint Scenarios
Finally, the proposed model is compared with SegNet-basic, and a near-optimal tuning of the network is compared to a selection of other state-of-art iris segmentation algorithms.
Smart Augmentation - Learning an Optimal Data Augmentation Strategy
Smart Augmentation works by creating a network that learns how to generate augmented data during the training process of a target network in a way that reduces that networks loss.
