5 citations · 10 across the 21 of their papers we have counts for
5 papers · 1 filter
A Semi-Paired Approach For Label-to-Image Translation
George Eskandar, Shuai Zhang, Mohamed Abdelsamad +3
Data efficiency, or the ability to generalize from a few labeled data, remains a major challenge in deep learning. Semi-supervised learning has thrived in traditional recognition t…
Towards Pragmatic Semantic Image Synthesis for Urban Scenes
George Eskandar, Diandian Guo, Karim Guirguis +1
The need for large amounts of training and validation data is a huge concern in scaling AI algorithms for autonomous driving. Semantic Image Synthesis (SIS), or label-to-image tran…
Wavelet-based Unsupervised Label-to-Image Translation
George Eskandar, Mohamed Abdelsamad, Karim Armanious +2
Semantic Image Synthesis (SIS) is a subclass of image-to-image translation where a semantic layout is used to generate a photorealistic image. State-of-the-art conditional Generati…
Urban-StyleGAN: Learning to Generate and Manipulate Images of Urban Scenes
George Eskandar, Youssef Farag, Tarun Yenamandra +3
A promise of Generative Adversarial Networks (GANs) is to provide cheap photorealistic data for training and validating AI models in autonomous driving. Despite their huge success,…
NIFF: Alleviating Forgetting in Generalized Few-Shot Object Detection via Neural Instance Feature Forging
Karim Guirguis, Johannes Meier, George Eskandar +3
Privacy and memory are two recurring themes in a broad conversation about the societal impact of AI. These concerns arise from the need for huge amounts of data to train deep neura…