  The state-of-the-art approaches in Generative Adversarial Networks (GANs) are
able to learn a mapping function from one image domain to another with unpaired
image data. However, these methods often produce artifacts and can only be able
to convert low-level information, but fail to transfer high-level semantic part
of images. The reason is mainly that generators do not have the ability to
detect the most discriminative semantic part of images, which thus makes the
generated images with low-quality. To handle the limitation, in this paper we
propose a novel Attention-Guided Generative Adversarial Network (AGGAN), which
can detect the most discriminative semantic object and minimize changes of
unwanted part for semantic manipulation problems without using extra data and
models. The attention-guided generators in AGGAN are able to produce attention
masks via a built-in attention mechanism, and then fuse the input image with
the attention mask to obtain a target image with high-quality. Moreover, we
propose a novel attention-guided discriminator which only considers attended
regions. The proposed AGGAN is trained by an end-to-end fashion with an
adversarial loss, cycle-consistency loss, pixel loss and attention loss. Both
qualitative and quantitative results demonstrate that our approach is effective
to generate sharper and more accurate images than existing models. The code is
available at https://github.com/Ha0Tang/AttentionGAN.
