146,667 research outputs found
Transferable knowledge for Low-cost Decision Making in Cloud Environments
Users of Infrastructure as a Service (IaaS) are increasingly overwhelmed with the wide range of providers and services offered by each
provider. As such, many users select services based on description alone. An emerging alternative is to use a decision support system (DSS), which
typically relies on gaining insights from observational data in order to assist a customer in making decisions regarding optimal deployment of cloud
applications. The primary activity of such systems is the generation of a prediction model (e.g. using machine learning), which requires a significantly
large amount of training data. However, considering the varying architectures of applications, cloud providers, and cloud offerings, this activity is
not sustainable as it incurs additional time and cost to collect data to train the models. We overcome this through developing a Transfer Learning (TL)
approach where knowledge (in the form of a prediction model and associated data set) gained from running an application on a particular IaaS is
transferred in order to substantially reduce the overhead of building new models for the performance of new applications and/or cloud infrastructures.
In this paper, we present our approach and evaluate it through extensive experimentation involving three real world applications over two major public
cloud providers, namely Amazon and Google. Our evaluation shows that our novel two-mode TL scheme increases overall efficiency with a factor of
60% reduction in the time and cost of generating a new prediction model. We test this under a number of cross-application and cross-cloud scenario
Interpretable and Generalizable Person Re-Identification with Query-Adaptive Convolution and Temporal Lifting
For person re-identification, existing deep networks often focus on
representation learning. However, without transfer learning, the learned model
is fixed as is, which is not adaptable for handling various unseen scenarios.
In this paper, beyond representation learning, we consider how to formulate
person image matching directly in deep feature maps. We treat image matching as
finding local correspondences in feature maps, and construct query-adaptive
convolution kernels on the fly to achieve local matching. In this way, the
matching process and results are interpretable, and this explicit matching is
more generalizable than representation features to unseen scenarios, such as
unknown misalignments, pose or viewpoint changes. To facilitate end-to-end
training of this architecture, we further build a class memory module to cache
feature maps of the most recent samples of each class, so as to compute image
matching losses for metric learning. Through direct cross-dataset evaluation,
the proposed Query-Adaptive Convolution (QAConv) method gains large
improvements over popular learning methods (about 10%+ mAP), and achieves
comparable results to many transfer learning methods. Besides, a model-free
temporal cooccurrence based score weighting method called TLift is proposed,
which improves the performance to a further extent, achieving state-of-the-art
results in cross-dataset person re-identification. Code is available at
https://github.com/ShengcaiLiao/QAConv.Comment: This is the ECCV 2020 version, including the appendi
Transductive Learning with String Kernels for Cross-Domain Text Classification
For many text classification tasks, there is a major problem posed by the
lack of labeled data in a target domain. Although classifiers for a target
domain can be trained on labeled text data from a related source domain, the
accuracy of such classifiers is usually lower in the cross-domain setting.
Recently, string kernels have obtained state-of-the-art results in various text
classification tasks such as native language identification or automatic essay
scoring. Moreover, classifiers based on string kernels have been found to be
robust to the distribution gap between different domains. In this paper, we
formally describe an algorithm composed of two simple yet effective
transductive learning approaches to further improve the results of string
kernels in cross-domain settings. By adapting string kernels to the test set
without using the ground-truth test labels, we report significantly better
accuracy rates in cross-domain English polarity classification.Comment: Accepted at ICONIP 2018. arXiv admin note: substantial text overlap
with arXiv:1808.0840
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