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28 Dec 2018: The segmen-tation is obtained by minimizing the clique loss function withTree-Reweighted [20] inference that uses the LBFGS opti-mization algorithm. ... 20] Justin Domke, “Learning graphical model parameterswith approximate marginal inference,” IEEE

28 Dec 2018: In each split, 20%of training images were used as a validation set for hyper-parameter selection. ... 20] G. Papandreou. Deep epitomic convolutional neuralnetworks. ArXiv:1406.6909, 2014. [21] A.

28 Dec 2018: Unsup:Unsupervised, Semi: Semi-supervised and Sup: Supervised. Dataset SHDL Semi Unsup SupCIFAR-10 83.90 83.3 [20] 82.9 [12] 96.2 [21]. ... 20] T. Salimans et al., “Improved techniques for traininggans,” ArXiv:1606.03498, 2016.

28 Dec 2018: b 20. 17. Fig. 1. Illustration shows the input image (x) of size 64 64 resized to images of resolution, x (64 64 ) and x′ (48 48) respectively.Image representations at

28 Dec 2018: cs. CV. ] 1. 0 Fe. b 20. 17. Fig. 1.

28 Dec 2018: front end to hybriddeep learning tasks[15, 20]. ... Sept. 2017,pp. 1–6. [20] Amarjot Singh. “ScatterNet Hybrid Frameworks forDeep Learning”.

23 Mar 2018: completion (20-23). Initially, in the domain of better design for elderly and. ... filtered, e.g., using a particle filter (20, 25). The filtered track is.