13,100 research outputs found
Learning Structured Inference Neural Networks with Label Relations
Images of scenes have various objects as well as abundant attributes, and
diverse levels of visual categorization are possible. A natural image could be
assigned with fine-grained labels that describe major components,
coarse-grained labels that depict high level abstraction or a set of labels
that reveal attributes. Such categorization at different concept layers can be
modeled with label graphs encoding label information. In this paper, we exploit
this rich information with a state-of-art deep learning framework, and propose
a generic structured model that leverages diverse label relations to improve
image classification performance. Our approach employs a novel stacked label
prediction neural network, capturing both inter-level and intra-level label
semantics. We evaluate our method on benchmark image datasets, and empirical
results illustrate the efficacy of our model.Comment: Conference on Computer Vision and Pattern Recognition(CVPR) 201
ECG Arrhythmia Classification Using Transfer Learning from 2-Dimensional Deep CNN Features
Due to the recent advances in the area of deep learning, it has been
demonstrated that a deep neural network, trained on a huge amount of data, can
recognize cardiac arrhythmias better than cardiologists. Moreover,
traditionally feature extraction was considered an integral part of ECG pattern
recognition; however, recent findings have shown that deep neural networks can
carry out the task of feature extraction directly from the data itself. In
order to use deep neural networks for their accuracy and feature extraction,
high volume of training data is required, which in the case of independent
studies is not pragmatic. To arise to this challenge, in this work, the
identification and classification of four ECG patterns are studied from a
transfer learning perspective, transferring knowledge learned from the image
classification domain to the ECG signal classification domain. It is
demonstrated that feature maps learned in a deep neural network trained on
great amounts of generic input images can be used as general descriptors for
the ECG signal spectrograms and result in features that enable classification
of arrhythmias. Overall, an accuracy of 97.23 percent is achieved in
classifying near 7000 instances by ten-fold cross validation.Comment: Accepted and presented for IEEE Biomedical Circuits and Systems
(BioCAS) on 17th-19th October 2018 in Ohio, US
A layered framework for pattern-based ontology evolution
The challenge of ontology-driven modelling of information
components is well known in both academia and industry. In this paper, we present a novel approach to deal with customisation and abstraction of ontology-based model evolution. As a result of an empirical study, we identify a layered change operator framework based on the granularity,
domain-speciļ¬city and abstraction of changes. The implementation of the operator framework is supported through layered change logs. Layered change logs capture the objective of ontology changes at a higher level of granularity and support a comprehensive understanding of ontology evolution. The layered change logs are formalised using a graph-based approach. We identify the recurrent ontology change patterns from an ontology change log for their reuse. The identiļ¬ed patterns facilitate optimizing and improving the deļ¬nition of domain-speciļ¬c change patterns
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
In this paper, we propose an Attentional Generative Adversarial Network
(AttnGAN) that allows attention-driven, multi-stage refinement for fine-grained
text-to-image generation. With a novel attentional generative network, the
AttnGAN can synthesize fine-grained details at different subregions of the
image by paying attentions to the relevant words in the natural language
description. In addition, a deep attentional multimodal similarity model is
proposed to compute a fine-grained image-text matching loss for training the
generator. The proposed AttnGAN significantly outperforms the previous state of
the art, boosting the best reported inception score by 14.14% on the CUB
dataset and 170.25% on the more challenging COCO dataset. A detailed analysis
is also performed by visualizing the attention layers of the AttnGAN. It for
the first time shows that the layered attentional GAN is able to automatically
select the condition at the word level for generating different parts of the
image
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