175 research outputs found
TET-GAN: Text Effects Transfer via Stylization and Destylization
Text effects transfer technology automatically makes the text dramatically
more impressive. However, previous style transfer methods either study the
model for general style, which cannot handle the highly-structured text effects
along the glyph, or require manual design of subtle matching criteria for text
effects. In this paper, we focus on the use of the powerful representation
abilities of deep neural features for text effects transfer. For this purpose,
we propose a novel Texture Effects Transfer GAN (TET-GAN), which consists of a
stylization subnetwork and a destylization subnetwork. The key idea is to train
our network to accomplish both the objective of style transfer and style
removal, so that it can learn to disentangle and recombine the content and
style features of text effects images. To support the training of our network,
we propose a new text effects dataset with as much as 64 professionally
designed styles on 837 characters. We show that the disentangled feature
representations enable us to transfer or remove all these styles on arbitrary
glyphs using one network. Furthermore, the flexible network design empowers
TET-GAN to efficiently extend to a new text style via one-shot learning where
only one example is required. We demonstrate the superiority of the proposed
method in generating high-quality stylized text over the state-of-the-art
methods.Comment: Accepted by AAAI 2019. Code and dataset will be available at
http://www.icst.pku.edu.cn/struct/Projects/TETGAN.htm
Global and Individualized Community Detection in Inhomogeneous Multilayer Networks
In network applications, it has become increasingly common to obtain datasets
in the form of multiple networks observed on the same set of subjects, where
each network is obtained in a related but different experiment condition or
application scenario. Such datasets can be modeled by multilayer networks where
each layer is a separate network itself while different layers are associated
and share some common information. The present paper studies community
detection in a stylized yet informative inhomogeneous multilayer network model.
In our model, layers are generated by different stochastic block models, the
community structures of which are (random) perturbations of a common global
structure while the connecting probabilities in different layers are not
related. Focusing on the symmetric two block case, we establish minimax rates
for both \emph{global estimation} of the common structure and
\emph{individualized estimation} of layer-wise community structures. Both
minimax rates have sharp exponents. In addition, we provide an efficient
algorithm that is simultaneously asymptotic minimax optimal for both estimation
tasks under mild conditions. The optimal rates depend on the \emph{parity} of
the number of most informative layers, a phenomenon that is caused by
inhomogeneity across layers.Comment: Corrected a few typos. 96 pages (main manuscript: 27 pages,
appendices: 69 pages), 5 figure
Rapid Invasion of Spartina Alterniflora in the Coastal Zone of Mainland China: Spatiotemporal Patterns and Human Prevention
Given the extensive spread and ecological consequences of exotic Spartina alterniflora (S. alterniflora) over the coast of mainland China, monitoring its spatiotemporal invasion patterns is important for the sake of coastal ecosystem management and ecological security. In this study, Landsat series images from 1990 to 2015 were used to establish multi-temporal datasets for documenting the temporal dynamics of S. alterniflora invasion. Our observations revealed that S. alterniflora had a continuous expansion with the area increasing by 50,204 ha during the considered 25 years. The largest expansion was identified in Jiangsu Province during the period of 1990-2000, and in Zhejiang Province during the periods 2000-2010 and 2010-2015. Three noticeable hotspots for S. alterniflora invasion were Yancheng of Jiangsu, Chongming of Shanghai, and Ningbo of Zhejiang, and each had a net area increase larger than 5000 ha. Moreover, an obvious shrinkage of S. alterniflora was identified in three coastal cities including the city of Cangzhou of Hebei, Dongguan, and Jiangmen of Guangdong. S. alterniflora invaded mostly into mudflats (>93%) and shrank primarily due to aquaculture (55.5%). This study sheds light on the historical spatial patterns in S. alterniflora distribution and thus is helpful for understanding its invasion mechanism and invasive species management
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