Deep learning's success has led to larger and larger models to handle more and more
complex tasks; trained models often contain millions of parameters. These large models are …

Deep learning's success has led to larger and larger models to handle more and more
complex tasks; trained models can contain millions of parameters. These large models are …

Generative adversarial networks (GANs) have gained increasing popularity in various
computer vision applications, and recently start to be deployed to resource-constrained …

More accurate machine learning models often demand more computation and memory at
test time, making them difficult to deploy on CPU-or memory-constrained devices. Model …

Neural network compression has recently received much attention due to the computational
requirements of modern deep models. In this work, our objective is to transfer knowledge …

We push forward neural network compression research by exploiting a novel challenging
task of large-scale conditional generative adversarial networks (GANs) compression. To this …

Generative adversarial networks (GANs) promote recent successes of deep learning in
fields such as computer vision and speech synthesis. However, training a GAN is notoriously …

The compression of Generative Adversarial Networks (GANs) has lately drawn attention,
due to the increasing demand for deploying GANs into mobile devices for numerous …

Generative Adversarial Networks (GAN) is a popular machine learning method that
possesses powerful image generation ability, which is useful for different multimedia …

We describe a simple and general neural network weight compression approach, in which
the network parameters (weights and biases) are represented in a" latent" space, amounting …