Papers › CycleGANAS: Differentiable Neural Architecture Search for CycleGAN

CycleGANAS: Differentiable Neural Architecture Search for CycleGAN

13 Nov 2023arXiv:2311.07162archive 2025-07-28

Taegun An, Changhee Joo

We develop a Neural Architecture Search (NAS) framework for CycleGAN that carries out unpaired image-to-image translation task. Extending previous NAS techniques for Generative Adversarial Networks (GANs) to CycleGAN is not straightforward due to the task difference and greater search space. We design architectures that consist of a stack of simple ResNet-based cells and develop a search method that effectively explore the large search space. We show that our framework, called CycleGANAS, not only effectively discovers high-performance architectures that either match or surpass the performance of the original CycleGAN, but also successfully address the data imbalance by individual architecture search for each translation direction. To our best knowledge, it is the first NAS result for CycleGAN and shed light on NAS for more complex structures.

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Tasks

Image-to-Image TranslationNeural Architecture SearchTranslation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Image-to-Image Translation horse2zebra CycleGANAS Frechet Inception Distance 38.06 #2 of 4 Archive leaderboard report
Image-to-Image Translation horse2zebra CycleGANAS Number of params 8.751M #2 of 4 Archive leaderboard report

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Methods

Batch NormalizationConvolutionCycle Consistency LossGAN Least Squares LossInstance NormalizationPatchGANReLUResidual BlockResidual ConnectionSigmoid ActivationTanh Activation

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