19,381 research outputs found
Semi-supervised Text Regression with Conditional Generative Adversarial Networks
Enormous online textual information provides intriguing opportunities for
understandings of social and economic semantics. In this paper, we propose a
novel text regression model based on a conditional generative adversarial
network (GAN), with an attempt to associate textual data and social outcomes in
a semi-supervised manner. Besides promising potential of predicting
capabilities, our superiorities are twofold: (i) the model works with
unbalanced datasets of limited labelled data, which align with real-world
scenarios; and (ii) predictions are obtained by an end-to-end framework,
without explicitly selecting high-level representations. Finally we point out
related datasets for experiments and future research directions
Age Progression/Regression by Conditional Adversarial Autoencoder
"If I provide you a face image of mine (without telling you the actual age
when I took the picture) and a large amount of face images that I crawled
(containing labeled faces of different ages but not necessarily paired), can
you show me what I would look like when I am 80 or what I was like when I was
5?" The answer is probably a "No." Most existing face aging works attempt to
learn the transformation between age groups and thus would require the paired
samples as well as the labeled query image. In this paper, we look at the
problem from a generative modeling perspective such that no paired samples is
required. In addition, given an unlabeled image, the generative model can
directly produce the image with desired age attribute. We propose a conditional
adversarial autoencoder (CAAE) that learns a face manifold, traversing on which
smooth age progression and regression can be realized simultaneously. In CAAE,
the face is first mapped to a latent vector through a convolutional encoder,
and then the vector is projected to the face manifold conditional on age
through a deconvolutional generator. The latent vector preserves personalized
face features (i.e., personality) and the age condition controls progression
vs. regression. Two adversarial networks are imposed on the encoder and
generator, respectively, forcing to generate more photo-realistic faces.
Experimental results demonstrate the appealing performance and flexibility of
the proposed framework by comparing with the state-of-the-art and ground truth.Comment: Accepted by The IEEE Conference on Computer Vision and Pattern
Recognition (CVPR 2017
Age Progression and Regression with Spatial Attention Modules
Age progression and regression refers to aesthetically render-ing a given
face image to present effects of face aging and rejuvenation, respectively.
Although numerous studies have been conducted in this topic, there are two
major problems: 1) multiple models are usually trained to simulate different
age mappings, and 2) the photo-realism of generated face images is heavily
influenced by the variation of training images in terms of pose, illumination,
and background. To address these issues, in this paper, we propose a framework
based on conditional Generative Adversarial Networks (cGANs) to achieve age
progression and regression simultaneously. Particularly, since face aging and
rejuvenation are largely different in terms of image translation patterns, we
model these two processes using two separate generators, each dedicated to one
age changing process. In addition, we exploit spatial attention mechanisms to
limit image modifications to regions closely related to age changes, so that
images with high visual fidelity could be synthesized for in-the-wild cases.
Experiments on multiple datasets demonstrate the ability of our model in
synthesizing lifelike face images at desired ages with personalized features
well preserved, and keeping age-irrelevant regions unchanged
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