24,659 research outputs found
Learning Latent Representations for Speech Generation and Transformation
An ability to model a generative process and learn a latent representation
for speech in an unsupervised fashion will be crucial to process vast
quantities of unlabelled speech data. Recently, deep probabilistic generative
models such as Variational Autoencoders (VAEs) have achieved tremendous success
in modeling natural images. In this paper, we apply a convolutional VAE to
model the generative process of natural speech. We derive latent space
arithmetic operations to disentangle learned latent representations. We
demonstrate the capability of our model to modify the phonetic content or the
speaker identity for speech segments using the derived operations, without the
need for parallel supervisory data.Comment: Accepted to Interspeech 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
A smart end-effector for assembly of space truss structures
A unique facility, the Automated Structures Research Laboratory, is being used to investigate robotic assembly of truss structures. A special-purpose end-effector is used to assemble structural elements into an eight meter diameter structure. To expand the capabilities of the facility to include construction of structures with curved surfaces from straight structural elements of different lengths, a new end-effector has been designed and fabricated. This end-effector contains an integrated microprocessor to monitor actuator operations through sensor feedback. This paper provides an overview of the automated assembly tasks required by this end-effector and a description of the new end-effector's hardware and control software
Adversarial Data Programming: Using GANs to Relax the Bottleneck of Curated Labeled Data
Paucity of large curated hand-labeled training data for every
domain-of-interest forms a major bottleneck in the deployment of machine
learning models in computer vision and other fields. Recent work (Data
Programming) has shown how distant supervision signals in the form of labeling
functions can be used to obtain labels for given data in near-constant time. In
this work, we present Adversarial Data Programming (ADP), which presents an
adversarial methodology to generate data as well as a curated aggregated label
has given a set of weak labeling functions. We validated our method on the
MNIST, Fashion MNIST, CIFAR 10 and SVHN datasets, and it outperformed many
state-of-the-art models. We conducted extensive experiments to study its
usefulness, as well as showed how the proposed ADP framework can be used for
transfer learning as well as multi-task learning, where data from two domains
are generated simultaneously using the framework along with the label
information. Our future work will involve understanding the theoretical
implications of this new framework from a game-theoretic perspective, as well
as explore the performance of the method on more complex datasets.Comment: CVPR 2018 main conference pape
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