6,490 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
ServeNet: A Deep Neural Network for Web Services Classification
Automated service classification plays a crucial role in service discovery,
selection, and composition. Machine learning has been widely used for service
classification in recent years. However, the performance of conventional
machine learning methods highly depends on the quality of manual feature
engineering. In this paper, we present a novel deep neural network to
automatically abstract low-level representation of both service name and
service description to high-level merged features without feature engineering
and the length limitation, and then predict service classification on 50
service categories. To demonstrate the effectiveness of our approach, we
conduct a comprehensive experimental study by comparing 10 machine learning
methods on 10,000 real-world web services. The result shows that the proposed
deep neural network can achieve higher accuracy in classification and more
robust than other machine learning methods.Comment: Accepted by ICWS'2
C2AE: Class Conditioned Auto-Encoder for Open-set Recognition
Models trained for classification often assume that all testing classes are
known while training. As a result, when presented with an unknown class during
testing, such closed-set assumption forces the model to classify it as one of
the known classes. However, in a real world scenario, classification models are
likely to encounter such examples. Hence, identifying those examples as unknown
becomes critical to model performance. A potential solution to overcome this
problem lies in a class of learning problems known as open-set recognition. It
refers to the problem of identifying the unknown classes during testing, while
maintaining performance on the known classes. In this paper, we propose an
open-set recognition algorithm using class conditioned auto-encoders with novel
training and testing methodology. In contrast to previous methods, training
procedure is divided in two sub-tasks, 1. closed-set classification and, 2.
open-set identification (i.e. identifying a class as known or unknown). Encoder
learns the first task following the closed-set classification training
pipeline, whereas decoder learns the second task by reconstructing conditioned
on class identity. Furthermore, we model reconstruction errors using the
Extreme Value Theory of statistical modeling to find the threshold for
identifying known/unknown class samples. Experiments performed on multiple
image classification datasets show proposed method performs significantly
better than state of the art.Comment: CVPR2019 (Oral
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