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It is well known that humans can learn and recognize objects effectively fromseveral limited image samples. However, learning from just a few images isstill a tremendous challenge for existing main-stream deep neural networks.Inspired by analogical reasoning in the human mind, a feasible strategy is totranslate the abundant images of a rich source domain to enrich the relevantyet different target domain with insufficient image data. To achieve this goal,we propose a novel, effective multi-adversarial framework (MA) based onpart-global learning, which accomplishes one-shot cross-domain image-to-imagetranslation. In specific, we first devise a part-global adversarial trainingscheme to provide an efficient way for feature extraction and preventdiscriminators being over-fitted. Then, a multi-adversarial mechanism isemployed to enhance the image-to-image translation ability to unearth thehigh-level semantic representation. Moreover, a balanced adversarial lossfunction is presented, which aims to balance the training data and stabilizethe training process. Extensive experiments demonstrate that the proposedapproach can obtain impressive results on various datasets between twoextremely imbalanced image domains and outperform state-of-the-art methods onone-shot image-to-image translation.

One-Shot Image-to-Image Translation via Part-Global Learning with a Multi-adversarial Framework