19,680 research outputs found
Generalization Bounds for Representative Domain Adaptation
In this paper, we propose a novel framework to analyze the theoretical
properties of the learning process for a representative type of domain
adaptation, which combines data from multiple sources and one target (or
briefly called representative domain adaptation). In particular, we use the
integral probability metric to measure the difference between the distributions
of two domains and meanwhile compare it with the H-divergence and the
discrepancy distance. We develop the Hoeffding-type, the Bennett-type and the
McDiarmid-type deviation inequalities for multiple domains respectively, and
then present the symmetrization inequality for representative domain
adaptation. Next, we use the derived inequalities to obtain the Hoeffding-type
and the Bennett-type generalization bounds respectively, both of which are
based on the uniform entropy number. Moreover, we present the generalization
bounds based on the Rademacher complexity. Finally, we analyze the asymptotic
convergence and the rate of convergence of the learning process for
representative domain adaptation. We discuss the factors that affect the
asymptotic behavior of the learning process and the numerical experiments
support our theoretical findings as well. Meanwhile, we give a comparison with
the existing results of domain adaptation and the classical results under the
same-distribution assumption.Comment: arXiv admin note: substantial text overlap with arXiv:1304.157
High strain rate and quasi-static compression behavior and energy absorption characteristic of PVC foam
The mechanical properties at room temperature of two densities PVC foams have been experimentally evaluated in both quasi-static and dynamic compression loading conditions. The strain rate effect have been evaluated by comparing the constant strength during plateau region. Energy absorption efficiency of PVC foam is investigated, and it shows that in certain density range, the efficiency of lighter PVC foam is larger than that of heavier PVC foam, but the efficiency stress of lighter PVC foam is smaller than that of heavier PVC foam. While the lighter PVC foam has been compressed more than heavier PVC foam when they reach their peak efficiency. Therefore, for a certain density of PVC foam itself, when the loading rates increase, the PVC foam will absorb more energy more efficiently
Distinguishing Computer-generated Graphics from Natural Images Based on Sensor Pattern Noise and Deep Learning
Computer-generated graphics (CGs) are images generated by computer software.
The~rapid development of computer graphics technologies has made it easier to
generate photorealistic computer graphics, and these graphics are quite
difficult to distinguish from natural images (NIs) with the naked eye. In this
paper, we propose a method based on sensor pattern noise (SPN) and deep
learning to distinguish CGs from NIs. Before being fed into our convolutional
neural network (CNN)-based model, these images---CGs and NIs---are clipped into
image patches. Furthermore, three high-pass filters (HPFs) are used to remove
low-frequency signals, which represent the image content. These filters are
also used to reveal the residual signal as well as SPN introduced by the
digital camera device. Different from the traditional methods of distinguishing
CGs from NIs, the proposed method utilizes a five-layer CNN to classify the
input image patches. Based on the classification results of the image patches,
we deploy a majority vote scheme to obtain the classification results for the
full-size images. The~experiments have demonstrated that (1) the proposed
method with three HPFs can achieve better results than that with only one HPF
or no HPF and that (2) the proposed method with three HPFs achieves 100\%
accuracy, although the NIs undergo a JPEG compression with a quality factor of
75.Comment: This paper has been published by Sensors. doi:10.3390/s18041296;
Sensors 2018, 18(4), 129
Bis(1H-imidazol-3-ium) naphthalene-1,5-disulfonate
The asymmetric unit of the title organic salt, 2C3H5N2
+·C10H6O6S2
2−, consists of an imidazolium cation and half a naphthalene-1,5-disulfonate dianion, completed to the full dianion through an inversion center. N—H⋯S and N—H⋯O hydrogen bonds link cations and anions in the crystal, forming a chain propagating along [101]
Bis(pyridinium) naphthalene-1,5-disulfonate dihydrate
The asymmetric unit of the title organic salt, 2C5H6N+·C10H6O6S2
2−·2H2O, consists of a pyridinium cation, half a naphthalene-1,5-disulfonate dianion and a water molecule. The dianion has a crystallographically imposed centre of symmetry. In the crystal, N—H⋯O and O—H⋯O hydrogen bonds link cations, anions and water molecules into a three-dimensional network
Multi Task Consistency Guided Source-Free Test-Time Domain Adaptation Medical Image Segmentation
Source-free test-time adaptation for medical image segmentation aims to
enhance the adaptability of segmentation models to diverse and previously
unseen test sets of the target domain, which contributes to the
generalizability and robustness of medical image segmentation models without
access to the source domain. Ensuring consistency between target edges and
paired inputs is crucial for test-time adaptation. To improve the performance
of test-time domain adaptation, we propose a multi task consistency guided
source-free test-time domain adaptation medical image segmentation method which
ensures the consistency of the local boundary predictions and the global
prototype representation. Specifically, we introduce a local boundary
consistency constraint method that explores the relationship between tissue
region segmentation and tissue boundary localization tasks. Additionally, we
propose a global feature consistency constraint toto enhance the intra-class
compactness. We conduct extensive experiments on the segmentation of benchmark
fundus images. Compared to prediction directly by the source domain model, the
segmentation Dice score is improved by 6.27\% and 0.96\% in RIM-ONE-r3 and
Drishti GS datasets, respectively. Additionally, the results of experiments
demonstrate that our proposed method outperforms existing competitive domain
adaptation segmentation algorithms.Comment: 31 pages,7 figure
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