  This paper studies the neural architecture search (NAS) problem for
developing efficient generator networks. Compared with deep models for visual
recognition tasks, generative adversarial network (GAN) are usually designed to
conduct various complex image generation. We first discover an intact search
space of generator networks including three dimensionalities, i.e., path,
operator, channel for fully excavating the network performance. To reduce the
huge search cost, we explore a coarse-to-fine search strategy which divides the
overall search process into three sub-optimization problems accordingly. In
addition, a fair supernet training approach is utilized to ensure that all
sub-networks can be updated fairly and stably. Experiments results on
benchmarks show that we can provide generator networks with better image
quality and lower computational costs over the state-of-the-art methods. For
example, with our method, it takes only about 8 GPU hours on the entire
edges-to-shoes dataset to get a 2.56 MB model with a 24.13 FID score and 10 GPU
hours on the entire Urban100 dataset to get a 1.49 MB model with a 24.94 PSNR
score.
