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A Light Weight Convolutional Neural Network for Single Image Super-Resolution
2020
Procedia Computer Science
Preserved Fulltext
In case of Fig. 3 (b), the LWSRNet obtains better SSIM value than that of MSRN [12] , proSR s [19] and SRFeat M [15] methods. ...
Moreover, the proposed LWSRNet model obtains such performance with a significantly less number of training parameters than that of CARN [2] , proSR s [19] and SRFeat M models. ...
doi:10.1016/j.procs.2020.04.015
fatcat:2bqfg2ra2vhljmk2yygsd4wanq
Deteksi Pemakaian Helm Proyek Dengan Metode Convolutional Neural Network
2021
Journal of Intelligent System and Computation
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Misraa, Meenu Agarwal, Ayushi Vyas, Vishal M. Chudasama dan Kishor P. ...
doi:10.52985/insyst.v3i1.157
fatcat:326wsvunefc7jn5y52dhnqkkpa
INCET 2020Conference Main Schedule
2020
2020 International Conference for Emerging Technology (INCET)
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Mahantesh Elemmi ( 99165
42252)
2
56
Meghal Darji, Jaivik Dave,
Nadim Asif, Chirag Godawat,
Vishal Chudasama and Kishor
Upla
Licence Plate Identification and
Recognition for Non-Helmeted
Motorcyclists ...
M V Mahendrakar (98862
71783)
2
459
Divyaang Agarwal, Vishrut
Gupta, Divyansh Jaiswal and
A.K. ...
doi:10.1109/incet49848.2020.9154153
fatcat:r5cis27hrvhanig7zoe2dom37a
Dopamine receptor signaling in the medial orbital frontal cortex and the acquisition and expression of fructose-conditioned flavor preferences in rats
2015
Brain Research
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., 2009) , whereas destruction of the mPFC and MFC differentially altered perseverative and premature responses to stimuli (Chudasama et al., 2003) . ...
Coronal 40-µm sections, stained with Cresyl violet, were examined with light microscopy by an observer unfamiliar with the behavioral data; all animals with confirmed appropriate bilateral cannulae placements ...
doi:10.1016/j.brainres.2014.11.028
pmid:25446441
pmcid:PMC4388031
fatcat:wxile3wcijhltgvmyur2ojn55e
Author Index
2014
Bulletin of Materials Science
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electrical resistivity in La 0⋅875 Sr 0⋅125 MnO 3 manganite nano-
structures
1095
Choudhary S
see Parida S K
1569
Choukchou-Braham A
see Bellifa A
669
Chowdhury Devasish
see Nath Bhabesh Kumar
1613
Chudasama ...
Gadhikar Aniruddha A
315
Sharma Ekta
see Sakalle Umesh Kumar
1339
Sharma Manoj
see Kaur Jagdeep
931
Sharma P K
see Singh Dharmendra Pratap
511
Sharma Prachi
see Verma Vivek
855
Sharma Vishal ...
doi:10.1007/s12034-014-0788-5
fatcat:6pn4qqgnyrfyvojgpldyl7zwoe
M2FNet: Multi-modal Fusion Network for Emotion Recognition in Conversation
[article]
2022
arXiv
pre-print
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F i f usionm = Φ m (... ...
AM + m dissim AM m sim AM = 1 + 2 e 4•D a,p s m dissim AM = 1 + 2 e −4•D a,n s +4 (11) In addition, we also utilize the variance loss function proposed by Bardes et al ...
arXiv:2206.02187v1
fatcat:gnywghmmgrbthegld3sejjwgbi
NTIRE 2020 Challenge on Real-World Image Super-Resolution: Methods and Results
[article]
2020
arXiv
pre-print
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. , m}, (1) where I D denotes the downsampled image, and k i refers to the specific blur kernel. ...
, Chudasama, Sardar Vallabhbhai National Institute Of Technology, Surat Kishor, Upla, Norwegian University of Science and Technology, Gjøvik, Norway Raghavendra, Ramachandra, Norwegian University of Science ...
arXiv:2005.01996v1
fatcat:ewngd7chdve3fbvwis32v64ruq
A Critical Review of Generative Adversarial Networks based on Stability Criteria
unpublished
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The convergence measure M global of the BEGAN model which correlates well with image fidelity. Here, model converges quickly. ...
David et al. also introduce a convergence measure M which is an indicator to measure network's performance. It is also used to control learning rate. ...
fatcat:bonvjglx3bcg5mzeeokxftw264
Cardiac Function and Heart Failure
2008
Journal of the American College of Cardiology
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Results: Patients showed increased PWV (7.4±2.9 vs 4.9±1.3 m/s, p<0.001) compared to controls. ...
Patients with advanced LV (restrictive or pseudonormal filling pattern) diastolic dysfunction showed increased PWV (8.6±2.5 vs 6.5± 2.9 m/s, p=0.008) compared to those with mild diastolic dysfunction . ...
Starling, Dennis M. ...
doi:10.1016/j.jacc.2008.02.004
fatcat:oqh4zi47xvh4hdielnxkyqu2eu