  It is well known that humans can learn and recognize objects effectively from
several limited image samples. However, learning from just a few images is
still a tremendous challenge for existing main-stream deep neural networks.
Inspired by analogical reasoning in the human mind, a feasible strategy is to
translate the abundant images of a rich source domain to enrich the relevant
yet different target domain with insufficient image data. To achieve this goal,
we propose a novel, effective multi-adversarial framework (MA) based on
part-global learning, which accomplishes one-shot cross-domain image-to-image
translation. In specific, we first devise a part-global adversarial training
scheme to provide an efficient way for feature extraction and prevent
discriminators being over-fitted. Then, a multi-adversarial mechanism is
employed to enhance the image-to-image translation ability to unearth the
high-level semantic representation. Moreover, a balanced adversarial loss
function is presented, which aims to balance the training data and stabilize
the training process. Extensive experiments demonstrate that the proposed
approach can obtain impressive results on various datasets between two
extremely imbalanced image domains and outperform state-of-the-art methods on
one-shot image-to-image translation.
