24,928 research outputs found
A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community
In recent years, deep learning (DL), a re-branding of neural networks (NNs),
has risen to the top in numerous areas, namely computer vision (CV), speech
recognition, natural language processing, etc. Whereas remote sensing (RS)
possesses a number of unique challenges, primarily related to sensors and
applications, inevitably RS draws from many of the same theories as CV; e.g.,
statistics, fusion, and machine learning, to name a few. This means that the RS
community should be aware of, if not at the leading edge of, of advancements
like DL. Herein, we provide the most comprehensive survey of state-of-the-art
RS DL research. We also review recent new developments in the DL field that can
be used in DL for RS. Namely, we focus on theories, tools and challenges for
the RS community. Specifically, we focus on unsolved challenges and
opportunities as it relates to (i) inadequate data sets, (ii)
human-understandable solutions for modelling physical phenomena, (iii) Big
Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and
learning algorithms for spectral, spatial and temporal data, (vi) transfer
learning, (vii) an improved theoretical understanding of DL systems, (viii)
high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote
Sensin
Direct Feedback Alignment with Sparse Connections for Local Learning
Recent advances in deep neural networks (DNNs) owe their success to training
algorithms that use backpropagation and gradient-descent. Backpropagation,
while highly effective on von Neumann architectures, becomes inefficient when
scaling to large networks. Commonly referred to as the weight transport
problem, each neuron's dependence on the weights and errors located deeper in
the network require exhaustive data movement which presents a key problem in
enhancing the performance and energy-efficiency of machine-learning hardware.
In this work, we propose a bio-plausible alternative to backpropagation drawing
from advances in feedback alignment algorithms in which the error computation
at a single synapse reduces to the product of three scalar values. Using a
sparse feedback matrix, we show that a neuron needs only a fraction of the
information previously used by the feedback alignment algorithms. Consequently,
memory and compute can be partitioned and distributed whichever way produces
the most efficient forward pass so long as a single error can be delivered to
each neuron. Our results show orders of magnitude improvement in data movement
and improvement in multiply-and-accumulate operations over
backpropagation. Like previous work, we observe that any variant of feedback
alignment suffers significant losses in classification accuracy on deep
convolutional neural networks. By transferring trained convolutional layers and
training the fully connected layers using direct feedback alignment, we
demonstrate that direct feedback alignment can obtain results competitive with
backpropagation. Furthermore, we observe that using an extremely sparse
feedback matrix, rather than a dense one, results in a small accuracy drop
while yielding hardware advantages. All the code and results are available
under https://github.com/bcrafton/ssdfa.Comment: 15 pages, 8 figure
A Survey on Deep Learning in Medical Image Analysis
Deep learning algorithms, in particular convolutional networks, have rapidly
become a methodology of choice for analyzing medical images. This paper reviews
the major deep learning concepts pertinent to medical image analysis and
summarizes over 300 contributions to the field, most of which appeared in the
last year. We survey the use of deep learning for image classification, object
detection, segmentation, registration, and other tasks and provide concise
overviews of studies per application area. Open challenges and directions for
future research are discussed.Comment: Revised survey includes expanded discussion section and reworked
introductory section on common deep architectures. Added missed papers from
before Feb 1st 201
Deep learning in remote sensing: a review
Standing at the paradigm shift towards data-intensive science, machine
learning techniques are becoming increasingly important. In particular, as a
major breakthrough in the field, deep learning has proven as an extremely
powerful tool in many fields. Shall we embrace deep learning as the key to all?
Or, should we resist a 'black-box' solution? There are controversial opinions
in the remote sensing community. In this article, we analyze the challenges of
using deep learning for remote sensing data analysis, review the recent
advances, and provide resources to make deep learning in remote sensing
ridiculously simple to start with. More importantly, we advocate remote sensing
scientists to bring their expertise into deep learning, and use it as an
implicit general model to tackle unprecedented large-scale influential
challenges, such as climate change and urbanization.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin
Efficient Yet Deep Convolutional Neural Networks for Semantic Segmentation
Semantic Segmentation using deep convolutional neural network pose more
complex challenge for any GPU intensive task. As it has to compute million of
parameters, it results to huge memory consumption. Moreover, extracting finer
features and conducting supervised training tends to increase the complexity.
With the introduction of Fully Convolutional Neural Network, which uses finer
strides and utilizes deconvolutional layers for upsampling, it has been a go to
for any image segmentation task. In this paper, we propose two segmentation
architecture which not only needs one-third the parameters to compute but also
gives better accuracy than the similar architectures. The model weights were
transferred from the popular neural net like VGG19 and VGG16 which were trained
on Imagenet classification data-set. Then we transform all the fully connected
layers to convolutional layers and use dilated convolution for decreasing the
parameters. Lastly, we add finer strides and attach four skip architectures
which are element-wise summed with the deconvolutional layers in steps. We
train and test on different sparse and fine data-sets like Pascal VOC2012,
Pascal-Context and NYUDv2 and show how better our model performs in this tasks.
On the other hand our model has a faster inference time and consumes less
memory for training and testing on NVIDIA Pascal GPUs, making it more efficient
and less memory consuming architecture for pixel-wise segmentation.Comment: 8 page
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