Deep neural models, in recent years, have been successful in almost every field, even
solving the most complex problem statements. However, these models are huge in size with …

Overparameterized networks trained to convergence have shown impressive performance
in domains such as computer vision and natural language processing. Pushing state of the …

In recent years, deep neural networks have been successful in both industry and academia,
especially for computer vision tasks. The great success of deep learning is mainly due to its …

Abstract Machine learning models deployed as a service (MLaaS) are susceptible to model
stealing attacks, where an adversary attempts to steal the model within a restricted access …

Knowledge distillation (KD), transferring knowledge from a cumbersome teacher model to a
lightweight student model, has been investigated to design efficient neural architectures …

Abstract Conditional Generative Adversarial Networks (cGANs) have enabled controllable
image synthesis for many computer vision and graphics applications. However, recent …

StyleGAN2 is a state-of-the-art network in generating realistic images. Besides, it was
explicitly trained to have disentangled directions in latent space, which allows efficient …

In recent years, many deep learning based image inpainting methods have been developed
by the research community. Some of those methods have shown impressive image …

During image editing, existing deep generative models tend to re-synthesize the entire
output from scratch, including the unedited regions. This leads to a significant waste of …

Generative adversarial networks (GANs) have enabled photorealistic image synthesis and
editing. However, due to the high computational cost of large-scale generators (eg …