DEGAS: Differentiable Efficient Generator Search

Understand

Network architecture search (NAS) achieves state-of-the-art results in various tasks such as classification and semantic segmentation.

• Recently, a reinforcement learning-based approach has been proposed for Generative Adversarial Networks (GANs) search.
• In this work, we propose an alternative strategy for GAN search by using a method called DEGAS (Differentiable Efficient GenerAtor Search), which focuses on efficiently finding the generator in the GAN.
• Our search algorithm is inspired by the differential architecture search strategy and the Global Latent Optimization (GLO) procedure.