4,835 research outputs found
DetOFA: Efficient Training of Once-for-All Networks for Object Detection by Using Pre-trained Supernet and Path Filter
We address the challenge of training a large supernet for the object
detection task, using a relatively small amount of training data. Specifically,
we propose an efficient supernet-based neural architecture search (NAS) method
that uses transfer learning and search space pruning. First, the supernet is
pre-trained on a classification task, for which large datasets are available.
Second, the search space defined by the supernet is pruned by removing
candidate models that are predicted to perform poorly. To effectively remove
the candidates over a wide range of resource constraints, we particularly
design a performance predictor, called path filter, which can accurately
predict the relative performance of the models that satisfy similar resource
constraints. Hence, supernet training is more focused on the best-performing
candidates. Our path filter handles prediction for paths with different
resource budgets. Compared to once-for-all, our proposed method reduces the
computational cost of the optimal network architecture by 30% and 63%, while
yielding better accuracy-floating point operations Pareto front (0.85 and 0.45
points of improvement on average precision for Pascal VOC and COCO,
respectively).Comment: Accepted to ICCV workshop 202
Deep Learning of Atomically Resolved Scanning Transmission Electron Microscopy Images: Chemical Identification and Tracking Local Transformations
Recent advances in scanning transmission electron and scanning probe
microscopies have opened exciting opportunities in probing the materials
structural parameters and various functional properties in real space with
angstrom-level precision. This progress has been accompanied by an exponential
increase in the size and quality of datasets produced by microscopic and
spectroscopic experimental techniques. These developments necessitate adequate
methods for extracting relevant physical and chemical information from the
large datasets, for which a priori information on the structures of various
atomic configurations and lattice defects is limited or absent. Here we
demonstrate an application of deep neural networks to extract information from
atomically resolved images including location of the atomic species and type of
defects. We develop a 'weakly-supervised' approach that uses information on the
coordinates of all atomic species in the image, extracted via a deep neural
network, to identify a rich variety of defects that are not part of an initial
training set. We further apply our approach to interpret complex atomic and
defect transformation, including switching between different coordination of
silicon dopants in graphene as a function of time, formation of peculiar
silicon dimer with mixed 3-fold and 4-fold coordination, and the motion of
molecular 'rotor'. This deep learning based approach resembles logic of a human
operator, but can be scaled leading to significant shift in the way of
extracting and analyzing information from raw experimental data
Towards Greener Solutions for Steering Angle Prediction
In this paper, we investigate the two most popular families of deep neural
architectures (i.e., ResNets and Inception nets) for the autonomous driving
task of steering angle prediction. This work provides preliminary evidence that
Inception architectures can perform as well or better than ResNet architectures
with less complexity for the autonomous driving task. Primary motivation
includes support for further research in smaller, more efficient neural network
architectures such that can not only accomplish complex tasks, such as steering
angle predictions, but also produce less carbon emissions, or, more succinctly,
neural networks that are more environmentally friendly. We look at various
sizes of ResNet and InceptionNet models to compare results. Our derived models
can achieve state-of-the-art results in terms of steering angle MSE
SuperNet in Neural Architecture Search: A Taxonomic Survey
Deep Neural Networks (DNN) have made significant progress in a wide range of
visual recognition tasks such as image classification, object detection, and
semantic segmentation. The evolution of convolutional architectures has led to
better performance by incurring expensive computational costs. In addition,
network design has become a difficult task, which is labor-intensive and
requires a high level of domain knowledge. To mitigate such issues, there have
been studies for a variety of neural architecture search methods that
automatically search for optimal architectures, achieving models with
impressive performance that outperform human-designed counterparts. This survey
aims to provide an overview of existing works in this field of research and
specifically focus on the supernet optimization that builds a neural network
that assembles all the architectures as its sub models by using weight sharing.
We aim to accomplish that by categorizing supernet optimization by proposing
them as solutions to the common challenges found in the literature: data-side
optimization, poor rank correlation alleviation, and transferable NAS for a
number of deployment scenarios
Intensity-based image registration using multiple distributed agents
Image registration is the process of geometrically aligning images taken from different sensors, viewpoints or instances in time. It plays a key role in the detection of defects or anomalies for automated visual inspection. A multiagent distributed blackboard system has been developed for intensity-based image registration. The images are divided into segments and allocated to agents on separate processors, allowing parallel computation of a similarity metric that measures the degree of likeness between reference and sensed images after the application of a transform. The need for a dedicated control module is removed by coordination of agents via the blackboard. Tests show that additional agents increase speed, provided the communication capacity of the blackboard is not saturated. The success of the approach in achieving registration, despite significant misalignment of the original images, is demonstrated in the detection of manufacturing defects on screen-printed plastic bottles and printed circuit boards
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