2 research outputs found
The Effectiveness of Data Augmentation in Image Classification using Deep Learning
In this paper, we explore and compare multiple solutions to the problem of
data augmentation in image classification. Previous work has demonstrated the
effectiveness of data augmentation through simple techniques, such as cropping,
rotating, and flipping input images. We artificially constrain our access to
data to a small subset of the ImageNet dataset, and compare each data
augmentation technique in turn. One of the more successful data augmentations
strategies is the traditional transformations mentioned above. We also
experiment with GANs to generate images of different styles. Finally, we
propose a method to allow a neural net to learn augmentations that best improve
the classifier, which we call neural augmentation. We discuss the successes and
shortcomings of this method on various datasets.Comment: 8 pages, 12 figure
TDEFSI: Theory Guided Deep Learning Based Epidemic Forecasting with Synthetic Information
Influenza-like illness (ILI) places a heavy social and economic burden on our
society. Traditionally, ILI surveillance data is updated weekly and provided at
a spatially coarse resolution. Producing timely and reliable high-resolution
spatiotemporal forecasts for ILI is crucial for local preparedness and optimal
interventions. We present TDEFSI (Theory Guided Deep Learning Based Epidemic
Forecasting with Synthetic Information), an epidemic forecasting framework that
integrates the strengths of deep neural networks and high-resolution
simulations of epidemic processes over networks. TDEFSI yields accurate
high-resolution spatiotemporal forecasts using low-resolution time series data.
During the training phase, TDEFSI uses high-resolution simulations of epidemics
that explicitly model spatial and social heterogeneity inherent in urban
regions as one component of training data. We train a two-branch recurrent
neural network model to take both within-season and between-season
low-resolution observations as features, and output high-resolution detailed
forecasts. The resulting forecasts are not just driven by observed data but
also capture the intricate social, demographic and geographic attributes of
specific urban regions and mathematical theories of disease propagation over
networks. We focus on forecasting the incidence of ILI and evaluate TDEFSI's
performance using synthetic and real-world testing datasets at the state and
county levels in the USA. The results show that, at the state level, our method
achieves comparable/better performance than several state-of-the-art methods.
At the county level, TDEFSI outperforms the other methods. The proposed method
can be applied to other infectious diseases as well.Comment: This article has been accepted by ACM TSAS journa