Recent years have witnessed a surge in the popularity of Machine Learning (ML), applied
across diverse domains. However, progress is impeded by the scarcity of training data due …

We describe a new training methodology for generative adversarial networks. The key idea
is to grow both the generator and discriminator progressively: starting from a low resolution …

Abstract Generative Adversarial Networks (GANs) can generate near photo realistic images
in narrow domains such as human faces. Yet, modeling complex distributions of datasets …

Neural architecture search (NAS) has witnessed prevailing success in image classification
and (very recently) segmentation tasks. In this paper, we present the first preliminary study …

Transferring the knowledge of pretrained networks to new domains by means of finetuning is
a widely used practice for applications based on discriminative models. To the best of our …

Humans are capable of learning new tasks without forgetting previous ones, while neural
networks fail due to catastrophic forgetting between new and previously-learned tasks. We …

Abstract Neural Architecture Search (NAS) that aims to automate the procedure of
architecture design has achieved promising results in many computer vision fields. In this …

Abstract Deep Convolutional Neural Networks (DCNNs) is currently the method of choice
both for generative, as well as for discriminative learning in computer vision and machine …

Deep convolutional neural networks (DCNNs) are currently the method of choice both for
generative, as well as for discriminative learning in computer vision and machine learning …

In this paper, we introduce a new reinforcement learning (RL) based neural architecture
search (NAS) methodology for effective and efficient generative adversarial network (GAN) …