229 research outputs found
Discriminative Region Proposal Adversarial Networks for High-Quality Image-to-Image Translation
Image-to-image translation has been made much progress with embracing
Generative Adversarial Networks (GANs). However, it's still very challenging
for translation tasks that require high quality, especially at high-resolution
and photorealism. In this paper, we present Discriminative Region Proposal
Adversarial Networks (DRPAN) for high-quality image-to-image translation. We
decompose the procedure of image-to-image translation task into three iterated
steps, first is to generate an image with global structure but some local
artifacts (via GAN), second is using our DRPnet to propose the most fake region
from the generated image, and third is to implement "image inpainting" on the
most fake region for more realistic result through a reviser, so that the
system (DRPAN) can be gradually optimized to synthesize images with more
attention on the most artifact local part. Experiments on a variety of
image-to-image translation tasks and datasets validate that our method
outperforms state-of-the-arts for producing high-quality translation results in
terms of both human perceptual studies and automatic quantitative measures.Comment: ECCV 201
GP-GAN: Gender Preserving GAN for Synthesizing Faces from Landmarks
Facial landmarks constitute the most compressed representation of faces and
are known to preserve information such as pose, gender and facial structure
present in the faces. Several works exist that attempt to perform high-level
face-related analysis tasks based on landmarks. In contrast, in this work, an
attempt is made to tackle the inverse problem of synthesizing faces from their
respective landmarks. The primary aim of this work is to demonstrate that
information preserved by landmarks (gender in particular) can be further
accentuated by leveraging generative models to synthesize corresponding faces.
Though the problem is particularly challenging due to its ill-posed nature, we
believe that successful synthesis will enable several applications such as
boosting performance of high-level face related tasks using landmark points and
performing dataset augmentation. To this end, a novel face-synthesis method
known as Gender Preserving Generative Adversarial Network (GP-GAN) that is
guided by adversarial loss, perceptual loss and a gender preserving loss is
presented. Further, we propose a novel generator sub-network UDeNet for GP-GAN
that leverages advantages of U-Net and DenseNet architectures. Extensive
experiments and comparison with recent methods are performed to verify the
effectiveness of the proposed method.Comment: 6 pages, 5 figures, this paper is accepted as 2018 24th International
Conference on Pattern Recognition (ICPR2018
Learning Compositional Visual Concepts with Mutual Consistency
Compositionality of semantic concepts in image synthesis and analysis is
appealing as it can help in decomposing known and generatively recomposing
unknown data. For instance, we may learn concepts of changing illumination,
geometry or albedo of a scene, and try to recombine them to generate physically
meaningful, but unseen data for training and testing. In practice however we
often do not have samples from the joint concept space available: We may have
data on illumination change in one data set and on geometric change in another
one without complete overlap. We pose the following question: How can we learn
two or more concepts jointly from different data sets with mutual consistency
where we do not have samples from the full joint space? We present a novel
answer in this paper based on cyclic consistency over multiple concepts,
represented individually by generative adversarial networks (GANs). Our method,
ConceptGAN, can be understood as a drop in for data augmentation to improve
resilience for real world applications. Qualitative and quantitative
evaluations demonstrate its efficacy in generating semantically meaningful
images, as well as one shot face verification as an example application.Comment: 10 pages, 8 figures, 4 tables, CVPR 201
MedGAN: Medical Image Translation using GANs
Image-to-image translation is considered a new frontier in the field of
medical image analysis, with numerous potential applications. However, a large
portion of recent approaches offers individualized solutions based on
specialized task-specific architectures or require refinement through
non-end-to-end training. In this paper, we propose a new framework, named
MedGAN, for medical image-to-image translation which operates on the image
level in an end-to-end manner. MedGAN builds upon recent advances in the field
of generative adversarial networks (GANs) by merging the adversarial framework
with a new combination of non-adversarial losses. We utilize a discriminator
network as a trainable feature extractor which penalizes the discrepancy
between the translated medical images and the desired modalities. Moreover,
style-transfer losses are utilized to match the textures and fine-structures of
the desired target images to the translated images. Additionally, we present a
new generator architecture, titled CasNet, which enhances the sharpness of the
translated medical outputs through progressive refinement via encoder-decoder
pairs. Without any application-specific modifications, we apply MedGAN on three
different tasks: PET-CT translation, correction of MR motion artefacts and PET
image denoising. Perceptual analysis by radiologists and quantitative
evaluations illustrate that the MedGAN outperforms other existing translation
approaches.Comment: 16 pages, 8 figure
xUnit: Learning a Spatial Activation Function for Efficient Image Restoration
In recent years, deep neural networks (DNNs) achieved unprecedented
performance in many low-level vision tasks. However, state-of-the-art results
are typically achieved by very deep networks, which can reach tens of layers
with tens of millions of parameters. To make DNNs implementable on platforms
with limited resources, it is necessary to weaken the tradeoff between
performance and efficiency. In this paper, we propose a new activation unit,
which is particularly suitable for image restoration problems. In contrast to
the widespread per-pixel activation units, like ReLUs and sigmoids, our unit
implements a learnable nonlinear function with spatial connections. This
enables the net to capture much more complex features, thus requiring a
significantly smaller number of layers in order to reach the same performance.
We illustrate the effectiveness of our units through experiments with
state-of-the-art nets for denoising, de-raining, and super resolution, which
are already considered to be very small. With our approach, we are able to
further reduce these models by nearly 50% without incurring any degradation in
performance.Comment: Conference on Computer Vision and Pattern Recognition (CVPR), 201
- …