163 research outputs found
Learning Generative ConvNets via Multi-grid Modeling and Sampling
This paper proposes a multi-grid method for learning energy-based generative
ConvNet models of images. For each grid, we learn an energy-based probabilistic
model where the energy function is defined by a bottom-up convolutional neural
network (ConvNet or CNN). Learning such a model requires generating synthesized
examples from the model. Within each iteration of our learning algorithm, for
each observed training image, we generate synthesized images at multiple grids
by initializing the finite-step MCMC sampling from a minimal 1 x 1 version of
the training image. The synthesized image at each subsequent grid is obtained
by a finite-step MCMC initialized from the synthesized image generated at the
previous coarser grid. After obtaining the synthesized examples, the parameters
of the models at multiple grids are updated separately and simultaneously based
on the differences between synthesized and observed examples. We show that this
multi-grid method can learn realistic energy-based generative ConvNet models,
and it outperforms the original contrastive divergence (CD) and persistent CD.Comment: CVPR 201
An Integrated Full-bridge Class-DE Ultrasound Transducer Driver for HIFU Applications
This thesis present a CMOS integrated transducer driver for high intensity focused ultrasound
(HIFU) applications. Because this driver will be used in a magnetic resonance imaging (MRI)
environment, no magnetic components such as inductors and transformers have been used in this
design. The transducer is directly connected to the driver without a matching network. The output
stage of this driver is a full-bridge Class DE RF amplifer which is able to deliver more power than
the previous design that has a half-bridge Class DE amplifer.
The driver was also designed to be used in a transducer array. A digital control unit was
integrated with the power amplifer that allows to program the drivers phase shift and duty ratio.
A strategy to drive a ultrasound transducer array using the designed driver is also presented in this
thesis.
This design was implemented using the AMS H35B4 CMOS technology using the Cadence suite
of design tools and occupies a die area of 2mm by 1.5mm with 20 input and output pads. Simulation
and initial experimental results are presented in this work. The proposed integrated CMOS driver
has an efficiency of 89.4% with 3.60 W of output power. Results are little bit different for each
transducer
Deformable Generator Network: Unsupervised Disentanglement of Appearance and Geometry
We present a deformable generator model to disentangle the appearance and
geometric information for both image and video data in a purely unsupervised
manner. The appearance generator network models the information related to
appearance, including color, illumination, identity or category, while the
geometric generator performs geometric warping, such as rotation and
stretching, through generating deformation field which is used to warp the
generated appearance to obtain the final image or video sequences. Two
generators take independent latent vectors as input to disentangle the
appearance and geometric information from image or video sequences. For video
data, a nonlinear transition model is introduced to both the appearance and
geometric generators to capture the dynamics over time. The proposed scheme is
general and can be easily integrated into different generative models. An
extensive set of qualitative and quantitative experiments shows that the
appearance and geometric information can be well disentangled, and the learned
geometric generator can be conveniently transferred to other image datasets to
facilitate knowledge transfer tasks.Comment: version
Motion-Based Generator Model: Unsupervised Disentanglement of Appearance, Trackable and Intrackable Motions in Dynamic Patterns
Dynamic patterns are characterized by complex spatial and motion patterns.
Understanding dynamic patterns requires a disentangled representational model
that separates the factorial components. A commonly used model for dynamic
patterns is the state space model, where the state evolves over time according
to a transition model and the state generates the observed image frames
according to an emission model. To model the motions explicitly, it is natural
for the model to be based on the motions or the displacement fields of the
pixels. Thus in the emission model, we let the hidden state generate the
displacement field, which warps the trackable component in the previous image
frame to generate the next frame while adding a simultaneously emitted residual
image to account for the change that cannot be explained by the deformation.
The warping of the previous image is about the trackable part of the change of
image frame, while the residual image is about the intrackable part of the
image. We use a maximum likelihood algorithm to learn the model that iterates
between inferring latent noise vectors that drive the transition model and
updating the parameters given the inferred latent vectors. Meanwhile we adopt a
regularization term to penalize the norms of the residual images to encourage
the model to explain the change of image frames by trackable motion. Unlike
existing methods on dynamic patterns, we learn our model in unsupervised
setting without ground truth displacement fields. In addition, our model
defines a notion of intrackability by the separation of warped component and
residual component in each image frame. We show that our method can synthesize
realistic dynamic pattern, and disentangling appearance, trackable and
intrackable motions. The learned models are useful for motion transfer, and it
is natural to adopt it to define and measure intrackability of a dynamic
pattern
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