2 research outputs found
Generative Adversarial Network with Multi-Branch Discriminator for Cross-Species Image-to-Image Translation
Current approaches have made great progress on image-to-image translation
tasks benefiting from the success of image synthesis methods especially
generative adversarial networks (GANs). However, existing methods are limited
to handling translation tasks between two species while keeping the content
matching on the semantic level. A more challenging task would be the
translation among more than two species. To explore this new area, we propose a
simple yet effective structure of a multi-branch discriminator for enhancing an
arbitrary generative adversarial architecture (GAN), named GAN-MBD. It takes
advantage of the boosting strategy to break a common discriminator into several
smaller ones with fewer parameters, which can enhance the generation and
synthesis abilities of GANs efficiently and effectively. Comprehensive
experiments show that the proposed multi-branch discriminator can dramatically
improve the performance of popular GANs on cross-species image-to-image
translation tasks while reducing the number of parameters for computation. The
code and some datasets are attached as supplementary materials for reference.Comment: 10 pages, 16 figure
One-Shot Image-to-Image Translation via Part-Global Learning with a Multi-adversarial Framework
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.Comment: 9 pages, 13 figure