2,046 research outputs found
Detail-Preserving Pooling in Deep Networks
Most convolutional neural networks use some method for gradually downscaling
the size of the hidden layers. This is commonly referred to as pooling, and is
applied to reduce the number of parameters, improve invariance to certain
distortions, and increase the receptive field size. Since pooling by nature is
a lossy process, it is crucial that each such layer maintains the portion of
the activations that is most important for the network's discriminability. Yet,
simple maximization or averaging over blocks, max or average pooling, or plain
downsampling in the form of strided convolutions are the standard. In this
paper, we aim to leverage recent results on image downscaling for the purposes
of deep learning. Inspired by the human visual system, which focuses on local
spatial changes, we propose detail-preserving pooling (DPP), an adaptive
pooling method that magnifies spatial changes and preserves important
structural detail. Importantly, its parameters can be learned jointly with the
rest of the network. We analyze some of its theoretical properties and show its
empirical benefits on several datasets and networks, where DPP consistently
outperforms previous pooling approaches.Comment: To appear at CVPR 201
Nonlocal Co-occurrence for Image Downscaling
Image downscaling is one of the widely used operations in image processing
and computer graphics. It was recently demonstrated in the literature that
kernel-based convolutional filters could be modified to develop efficient image
downscaling algorithms. In this work, we present a new downscaling technique
which is based on kernel-based image filtering concept. We propose to use
pairwise co-occurrence similarity of the pixelpairs as the range kernel
similarity in the filtering operation. The co-occurrence of the pixel-pair is
learned directly from the input image. This co-occurrence learning is performed
in a neighborhood based fashion all over the image. The proposed method can
preserve the high-frequency structures, which were present in the input image,
into the downscaled image. The resulting images retain visually important
details and do not suffer from edge-blurring artifact. We demonstrate the
effectiveness of our proposed approach with extensive experiments on a large
number of images downscaled with various downscaling factors.Comment: 9 pages, 8 figure
Variational Downscaling, Fusion and Assimilation of Hydrometeorological States via Regularized Estimation
Improved estimation of hydrometeorological states from down-sampled
observations and background model forecasts in a noisy environment, has been a
subject of growing research in the past decades. Here, we introduce a unified
framework that ties together the problems of downscaling, data fusion and data
assimilation as ill-posed inverse problems. This framework seeks solutions
beyond the classic least squares estimation paradigms by imposing proper
regularization, which are constraints consistent with the degree of smoothness
and probabilistic structure of the underlying state. We review relevant
regularization methods in derivative space and extend classic formulations of
the aforementioned problems with particular emphasis on hydrologic and
atmospheric applications. Informed by the statistical characteristics of the
state variable of interest, the central results of the paper suggest that
proper regularization can lead to a more accurate and stable recovery of the
true state and hence more skillful forecasts. In particular, using the Tikhonov
and Huber regularization in the derivative space, the promise of the proposed
framework is demonstrated in static downscaling and fusion of synthetic
multi-sensor precipitation data, while a data assimilation numerical experiment
is presented using the heat equation in a variational setting
Downsampling methods for medical datasets
Volume visualization software usually has to deal with datasets that are larger than the GPUs may hold. This is especially true in one of the most popular application scenarios: medical visualization. Typically, medical datasets are available for different personnel, but only radiologists have high-end systems that are able to cope with large data. For the rest of physicians, usually low-end systems are only available. As a result, most volume rendering packages downsample the data prior to uploading to the GPU. The most common approach consists in performing iterative subsampling along the longest axis, until the model fits inside the GPU memory. This causes important information loss that affects the final rendering. Some cleverer techniques may be developed to preserve the volumetric information. In this paper we explore the quality of different downsampling methods and present a new approach that produces smooth lower-resolution representations, yet still preserves small features that are prone to disappear with other approaches.Peer ReviewedPostprint (published version
Development of inventory datasets through remote sensing and direct observation data for earthquake loss estimation
This report summarizes the lessons learnt in extracting exposure information for the three study sites, Thessaloniki, Vienna and Messina that were addressed in SYNER-G. Fine scale information on exposed elements that for SYNER-G include buildings, civil engineering works and population, is one of the variables used to quantify risk. Collecting data and creating exposure inventories is a very time-demanding job and all possible data-gathering techniques should be used to address the data shortcoming problem. This report focuses on combining direct observation and remote sensing data for the development of exposure models for seismic risk assessment. In this report a summary of the methods for collecting, processing and archiving inventory datasets is provided in Chapter 2. Chapter 3 deals with the integration of different data sources for optimum inventory datasets, whilst Chapters 4, 5 and 6 provide some case studies where combinations between direct observation and remote sensing have been used. The cities of Vienna (Austria), Thessaloniki (Greece) and Messina (Italy) have been chosen to test the proposed approaches.JRC.G.5-European laboratory for structural assessmen
UrbanFM: Inferring Fine-Grained Urban Flows
Urban flow monitoring systems play important roles in smart city efforts
around the world. However, the ubiquitous deployment of monitoring devices,
such as CCTVs, induces a long-lasting and enormous cost for maintenance and
operation. This suggests the need for a technology that can reduce the number
of deployed devices, while preventing the degeneration of data accuracy and
granularity. In this paper, we aim to infer the real-time and fine-grained
crowd flows throughout a city based on coarse-grained observations. This task
is challenging due to two reasons: the spatial correlations between coarse- and
fine-grained urban flows, and the complexities of external impacts. To tackle
these issues, we develop a method entitled UrbanFM based on deep neural
networks. Our model consists of two major parts: 1) an inference network to
generate fine-grained flow distributions from coarse-grained inputs by using a
feature extraction module and a novel distributional upsampling module; 2) a
general fusion subnet to further boost the performance by considering the
influences of different external factors. Extensive experiments on two
real-world datasets, namely TaxiBJ and HappyValley, validate the effectiveness
and efficiency of our method compared to seven baselines, demonstrating the
state-of-the-art performance of our approach on the fine-grained urban flow
inference problem
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