90,366 research outputs found
Deep Over-sampling Framework for Classifying Imbalanced Data
Class imbalance is a challenging issue in practical classification problems
for deep learning models as well as traditional models. Traditionally
successful countermeasures such as synthetic over-sampling have had limited
success with complex, structured data handled by deep learning models. In this
paper, we propose Deep Over-sampling (DOS), a framework for extending the
synthetic over-sampling method to exploit the deep feature space acquired by a
convolutional neural network (CNN). Its key feature is an explicit, supervised
representation learning, for which the training data presents each raw input
sample with a synthetic embedding target in the deep feature space, which is
sampled from the linear subspace of in-class neighbors. We implement an
iterative process of training the CNN and updating the targets, which induces
smaller in-class variance among the embeddings, to increase the discriminative
power of the deep representation. We present an empirical study using public
benchmarks, which shows that the DOS framework not only counteracts class
imbalance better than the existing method, but also improves the performance of
the CNN in the standard, balanced settings
COCO_TS Dataset: Pixel-level Annotations Based on Weak Supervision for Scene Text Segmentation
The absence of large scale datasets with pixel-level supervisions is a
significant obstacle for the training of deep convolutional networks for scene
text segmentation. For this reason, synthetic data generation is normally
employed to enlarge the training dataset. Nonetheless, synthetic data cannot
reproduce the complexity and variability of natural images. In this paper, a
weakly supervised learning approach is used to reduce the shift between
training on real and synthetic data. Pixel-level supervisions for a text
detection dataset (i.e. where only bounding-box annotations are available) are
generated. In particular, the COCO-Text-Segmentation (COCO_TS) dataset, which
provides pixel-level supervisions for the COCO-Text dataset, is created and
released. The generated annotations are used to train a deep convolutional
neural network for semantic segmentation. Experiments show that the proposed
dataset can be used instead of synthetic data, allowing us to use only a
fraction of the training samples and significantly improving the performances
The applications of deep neural networks to sdBV classification
With several new large-scale surveys on the horizon, including LSST, TESS,
ZTF, and Evryscope, faster and more accurate analysis methods will be required
to adequately process the enormous amount of data produced. Deep learning, used
in industry for years now, allows for advanced feature detection in minimally
prepared datasets at very high speeds; however, despite the advantages of this
method, its application to astrophysics has not yet been extensively explored.
This dearth may be due to a lack of training data available to researchers.
Here we generate synthetic data loosely mimicking the properties of acoustic
mode pulsating stars and we show that two separate paradigms of deep learning -
the Artificial Neural Network And the Convolutional Neural Network - can both
be used to classify this synthetic data effectively. And that additionally this
classification can be performed at relatively high levels of accuracy with
minimal time spent adjusting network hyperparameters.Comment: 12 pages, 10 figures, originally presented at sdOB
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