4,304 research outputs found
Bidirectional Conditional Generative Adversarial Networks
Conditional Generative Adversarial Networks (cGANs) are generative models
that can produce data samples () conditioned on both latent variables ()
and known auxiliary information (). We propose the Bidirectional cGAN
(BiCoGAN), which effectively disentangles and in the generation process
and provides an encoder that learns inverse mappings from to both and
, trained jointly with the generator and the discriminator. We present
crucial techniques for training BiCoGANs, which involve an extrinsic factor
loss along with an associated dynamically-tuned importance weight. As compared
to other encoder-based cGANs, BiCoGANs encode more accurately, and utilize
and more effectively and in a more disentangled way to generate
samples.Comment: To appear in Proceedings of ACCV 201
Generative Adversarial Networks (GANs): Challenges, Solutions, and Future Directions
Generative Adversarial Networks (GANs) is a novel class of deep generative
models which has recently gained significant attention. GANs learns complex and
high-dimensional distributions implicitly over images, audio, and data.
However, there exists major challenges in training of GANs, i.e., mode
collapse, non-convergence and instability, due to inappropriate design of
network architecture, use of objective function and selection of optimization
algorithm. Recently, to address these challenges, several solutions for better
design and optimization of GANs have been investigated based on techniques of
re-engineered network architectures, new objective functions and alternative
optimization algorithms. To the best of our knowledge, there is no existing
survey that has particularly focused on broad and systematic developments of
these solutions. In this study, we perform a comprehensive survey of the
advancements in GANs design and optimization solutions proposed to handle GANs
challenges. We first identify key research issues within each design and
optimization technique and then propose a new taxonomy to structure solutions
by key research issues. In accordance with the taxonomy, we provide a detailed
discussion on different GANs variants proposed within each solution and their
relationships. Finally, based on the insights gained, we present the promising
research directions in this rapidly growing field.Comment: 42 pages, Figure 13, Table
Using Photorealistic Face Synthesis and Domain Adaptation to Improve Facial Expression Analysis
Cross-domain synthesizing realistic faces to learn deep models has attracted
increasing attention for facial expression analysis as it helps to improve the
performance of expression recognition accuracy despite having small number of
real training images. However, learning from synthetic face images can be
problematic due to the distribution discrepancy between low-quality synthetic
images and real face images and may not achieve the desired performance when
the learned model applies to real world scenarios. To this end, we propose a
new attribute guided face image synthesis to perform a translation between
multiple image domains using a single model. In addition, we adopt the proposed
model to learn from synthetic faces by matching the feature distributions
between different domains while preserving each domain's characteristics. We
evaluate the effectiveness of the proposed approach on several face datasets on
generating realistic face images. We demonstrate that the expression
recognition performance can be enhanced by benefiting from our face synthesis
model. Moreover, we also conduct experiments on a near-infrared dataset
containing facial expression videos of drivers to assess the performance using
in-the-wild data for driver emotion recognition.Comment: 8 pages, 8 figures, 5 tables, accepted by FG 2019. arXiv admin note:
substantial text overlap with arXiv:1905.0028
Learn to synthesize and synthesize to learn
Attribute guided face image synthesis aims to manipulate attributes on a face
image. Most existing methods for image-to-image translation can either perform
a fixed translation between any two image domains using a single attribute or
require training data with the attributes of interest for each subject.
Therefore, these methods could only train one specific model for each pair of
image domains, which limits their ability in dealing with more than two
domains. Another disadvantage of these methods is that they often suffer from
the common problem of mode collapse that degrades the quality of the generated
images. To overcome these shortcomings, we propose attribute guided face image
generation method using a single model, which is capable to synthesize multiple
photo-realistic face images conditioned on the attributes of interest. In
addition, we adopt the proposed model to increase the realism of the simulated
face images while preserving the face characteristics. Compared to existing
models, synthetic face images generated by our method present a good
photorealistic quality on several face datasets. Finally, we demonstrate that
generated facial images can be used for synthetic data augmentation, and
improve the performance of the classifier used for facial expression
recognition.Comment: Accepted to Computer Vision and Image Understanding (CVIU
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