11,451 research outputs found
Harvesting Discriminative Meta Objects with Deep CNN Features for Scene Classification
Recent work on scene classification still makes use of generic CNN features
in a rudimentary manner. In this ICCV 2015 paper, we present a novel pipeline
built upon deep CNN features to harvest discriminative visual objects and parts
for scene classification. We first use a region proposal technique to generate
a set of high-quality patches potentially containing objects, and apply a
pre-trained CNN to extract generic deep features from these patches. Then we
perform both unsupervised and weakly supervised learning to screen these
patches and discover discriminative ones representing category-specific objects
and parts. We further apply discriminative clustering enhanced with local CNN
fine-tuning to aggregate similar objects and parts into groups, called meta
objects. A scene image representation is constructed by pooling the feature
response maps of all the learned meta objects at multiple spatial scales. We
have confirmed that the scene image representation obtained using this new
pipeline is capable of delivering state-of-the-art performance on two popular
scene benchmark datasets, MIT Indoor 67~\cite{MITIndoor67} and
Sun397~\cite{Sun397}Comment: To Appear in ICCV 201
Online Fault Classification in HPC Systems through Machine Learning
As High-Performance Computing (HPC) systems strive towards the exascale goal,
studies suggest that they will experience excessive failure rates. For this
reason, detecting and classifying faults in HPC systems as they occur and
initiating corrective actions before they can transform into failures will be
essential for continued operation. In this paper, we propose a fault
classification method for HPC systems based on machine learning that has been
designed specifically to operate with live streamed data. We cast the problem
and its solution within realistic operating constraints of online use. Our
results show that almost perfect classification accuracy can be reached for
different fault types with low computational overhead and minimal delay. We
have based our study on a local dataset, which we make publicly available, that
was acquired by injecting faults to an in-house experimental HPC system.Comment: Accepted for publication at the Euro-Par 2019 conferenc
Partial Transfer Learning with Selective Adversarial Networks
Adversarial learning has been successfully embedded into deep networks to
learn transferable features, which reduce distribution discrepancy between the
source and target domains. Existing domain adversarial networks assume fully
shared label space across domains. In the presence of big data, there is strong
motivation of transferring both classification and representation models from
existing big domains to unknown small domains. This paper introduces partial
transfer learning, which relaxes the shared label space assumption to that the
target label space is only a subspace of the source label space. Previous
methods typically match the whole source domain to the target domain, which are
prone to negative transfer for the partial transfer problem. We present
Selective Adversarial Network (SAN), which simultaneously circumvents negative
transfer by selecting out the outlier source classes and promotes positive
transfer by maximally matching the data distributions in the shared label
space. Experiments demonstrate that our models exceed state-of-the-art results
for partial transfer learning tasks on several benchmark datasets
Learning and Matching Multi-View Descriptors for Registration of Point Clouds
Critical to the registration of point clouds is the establishment of a set of
accurate correspondences between points in 3D space. The correspondence problem
is generally addressed by the design of discriminative 3D local descriptors on
the one hand, and the development of robust matching strategies on the other
hand. In this work, we first propose a multi-view local descriptor, which is
learned from the images of multiple views, for the description of 3D keypoints.
Then, we develop a robust matching approach, aiming at rejecting outlier
matches based on the efficient inference via belief propagation on the defined
graphical model. We have demonstrated the boost of our approaches to
registration on the public scanning and multi-view stereo datasets. The
superior performance has been verified by the intensive comparisons against a
variety of descriptors and matching methods
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