3,208 research outputs found
Raiders of the Lost Architecture: Kernels for Bayesian Optimization in Conditional Parameter Spaces
In practical Bayesian optimization, we must often search over structures with
differing numbers of parameters. For instance, we may wish to search over
neural network architectures with an unknown number of layers. To relate
performance data gathered for different architectures, we define a new kernel
for conditional parameter spaces that explicitly includes information about
which parameters are relevant in a given structure. We show that this kernel
improves model quality and Bayesian optimization results over several simpler
baseline kernels.Comment: 6 pages, 3 figures. Appeared in the NIPS 2013 workshop on Bayesian
optimizatio
MobileNetV2: Inverted Residuals and Linear Bottlenecks
In this paper we describe a new mobile architecture, MobileNetV2, that
improves the state of the art performance of mobile models on multiple tasks
and benchmarks as well as across a spectrum of different model sizes. We also
describe efficient ways of applying these mobile models to object detection in
a novel framework we call SSDLite. Additionally, we demonstrate how to build
mobile semantic segmentation models through a reduced form of DeepLabv3 which
we call Mobile DeepLabv3.
The MobileNetV2 architecture is based on an inverted residual structure where
the input and output of the residual block are thin bottleneck layers opposite
to traditional residual models which use expanded representations in the input
an MobileNetV2 uses lightweight depthwise convolutions to filter features in
the intermediate expansion layer. Additionally, we find that it is important to
remove non-linearities in the narrow layers in order to maintain
representational power. We demonstrate that this improves performance and
provide an intuition that led to this design. Finally, our approach allows
decoupling of the input/output domains from the expressiveness of the
transformation, which provides a convenient framework for further analysis. We
measure our performance on Imagenet classification, COCO object detection, VOC
image segmentation. We evaluate the trade-offs between accuracy, and number of
operations measured by multiply-adds (MAdd), as well as the number of
parameter
Gaussian Processes with Context-Supported Priors for Active Object Localization
We devise an algorithm using a Bayesian optimization framework in conjunction
with contextual visual data for the efficient localization of objects in still
images. Recent research has demonstrated substantial progress in object
localization and related tasks for computer vision. However, many current
state-of-the-art object localization procedures still suffer from inaccuracy
and inefficiency, in addition to failing to provide a principled and
interpretable system amenable to high-level vision tasks. We address these
issues with the current research.
Our method encompasses an active search procedure that uses contextual data
to generate initial bounding-box proposals for a target object. We train a
convolutional neural network to approximate an offset distance from the target
object. Next, we use a Gaussian Process to model this offset response signal
over the search space of the target. We then employ a Bayesian active search
for accurate localization of the target.
In experiments, we compare our approach to a state-of-theart bounding-box
regression method for a challenging pedestrian localization task. Our method
exhibits a substantial improvement over this baseline regression method.Comment: 10 pages, 4 figure
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