692 research outputs found
Detection of Inferior Myocardial Infarction using Shallow Convolutional Neural Networks
Myocardial Infarction is one of the leading causes of death worldwide. This
paper presents a Convolutional Neural Network (CNN) architecture which takes
raw Electrocardiography (ECG) signal from lead II, III and AVF and
differentiates between inferior myocardial infarction (IMI) and healthy
signals. The performance of the model is evaluated on IMI and healthy signals
obtained from Physikalisch-Technische Bundesanstalt (PTB) database. A
subject-oriented approach is taken to comprehend the generalization capability
of the model and compared with the current state of the art. In a
subject-oriented approach, the network is tested on one patient and trained on
rest of the patients. Our model achieved a superior metrics scores (accuracy=
84.54%, sensitivity= 85.33% and specificity= 84.09%) when compared to the
benchmark. We also analyzed the discriminating strength of the features
extracted by the convolutional layers by means of geometric separability index
and euclidean distance and compared it with the benchmark model
A Style-Based Generator Architecture for Generative Adversarial Networks
We propose an alternative generator architecture for generative adversarial
networks, borrowing from style transfer literature. The new architecture leads
to an automatically learned, unsupervised separation of high-level attributes
(e.g., pose and identity when trained on human faces) and stochastic variation
in the generated images (e.g., freckles, hair), and it enables intuitive,
scale-specific control of the synthesis. The new generator improves the
state-of-the-art in terms of traditional distribution quality metrics, leads to
demonstrably better interpolation properties, and also better disentangles the
latent factors of variation. To quantify interpolation quality and
disentanglement, we propose two new, automated methods that are applicable to
any generator architecture. Finally, we introduce a new, highly varied and
high-quality dataset of human faces.Comment: CVPR 2019 final versio
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