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Conditional Generative Adversarial Networks (cGANs) have enabled controllable image synthesis for many vision and graphics applications. However, recent cGANs are 1-2 orders of magnitude more compute-intensive than modern recognition CNNs. For example, GauGAN consumes 281G MACs per image, compared to 0.44G MACs for MobileNet-v3, making it difficult for interactive deployment. In this work, we propose a general-purpose compression framework for reducing the inference time and model size of the generator in cGANs. Directly applying existing compression methods yields poor performance due to the difficulty of GAN training and the differences in generator architectures. We address these challenges in two ways. First, to stabilize GAN training, we transfer knowledge of multiple intermediate representations of the original model to its compressed model and unify unpaired and paired learning. Second, instead of reusing existing CNN designs, our method finds efficient architectures via neural architecture search. To accelerate the search process, we decouple the model training and search via weight sharing. Experiments demonstrate the effectiveness of our method across different supervision settings, network architectures, and learning methods. Without losing image quality, we reduce the computation of CycleGAN by 21×, Pix2pix by 12×, MUNIT by 29×, and GauGAN by 9×, paving the way for interactive image synthesis. [ABSTRACT FROM AUTHOR]

In this paper, the LP11 mode evolution in a few mode-polarization maintaining fiber is experimentally investigated. Selective conversion from the LP11a/LP11b mode to the LP11b/LP11a , OAM+1, and OAM−1 modes are demonstrated with varying wavelengths and fiber lengths. This property may have nice applications in spatial division multiplexing (SDM) systems, and one of them, i.e., the all-fiber compact and robust scalar orbital angular momentum (OAM) generator, which is cascaded by a mode-selective coupler and a few mode-polarization maintaining fiber, is also demonstrated with expected performance for conversion between LP01 mode and OAM+1/OAM−1. [ABSTRACT FROM PUBLISHER]