58 research outputs found
Learning Residual Images for Face Attribute Manipulation
Face attributes are interesting due to their detailed description of human
faces. Unlike prior researches working on attribute prediction, we address an
inverse and more challenging problem called face attribute manipulation which
aims at modifying a face image according to a given attribute value. Instead of
manipulating the whole image, we propose to learn the corresponding residual
image defined as the difference between images before and after the
manipulation. In this way, the manipulation can be operated efficiently with
modest pixel modification. The framework of our approach is based on the
Generative Adversarial Network. It consists of two image transformation
networks and a discriminative network. The transformation networks are
responsible for the attribute manipulation and its dual operation and the
discriminative network is used to distinguish the generated images from real
images. We also apply dual learning to allow transformation networks to learn
from each other. Experiments show that residual images can be effectively
learned and used for attribute manipulations. The generated images remain most
of the details in attribute-irrelevant areas
A Double Joint Bayesian Approach for J-Vector Based Text-dependent Speaker Verification
J-vector has been proved to be very effective in text-dependent speaker
verification with short-duration speech. However, the current state-of-the-art
back-end classifiers, e.g. joint Bayesian model, cannot make full use of such
deep features. In this paper, we generalize the standard joint Bayesian
approach to model the multi-faceted information in the j-vector explicitly and
jointly. In our generalization, the j-vector was modeled as a result derived by
a generative Double Joint Bayesian (DoJoBa) model, which contains several kinds
of latent variables. With DoJoBa, we are able to explicitly build a model that
can combine multiple heterogeneous information from the j-vectors. In
verification step, we calculated the likelihood to describe whether the two
j-vectors having consistent labels or not. On the public RSR2015 data corpus,
the experimental results showed that our approach can achieve 0.02\% EER and
0.02\% EER for impostor wrong and impostor correct cases respectively
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