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14. Learning Smooth Representation for Unsupervised Domain Adaptation

Author

Cai, Guanyu, He, Lianghua, Zhou, Mengchu, Alhumade, Hesham, and Hu, Die

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Typical adversarial-training-based unsupervised domain adaptation methods are vulnerable when the source and target datasets are highly-complex or exhibit a large discrepancy between their data distributions. Recently, several Lipschitz-constraint-based methods have been explored. The satisfaction of Lipschitz continuity guarantees a remarkable performance on a target domain. However, they lack a mathematical analysis of why a Lipschitz constraint is beneficial to unsupervised domain adaptation and usually perform poorly on large-scale datasets. In this paper, we take the principle of utilizing a Lipschitz constraint further by discussing how it affects the error bound of unsupervised domain adaptation. A connection between them is built and an illustration of how Lipschitzness reduces the error bound is presented. A \textbf{local smooth discrepancy} is defined to measure Lipschitzness of a target distribution in a pointwise way. When constructing a deep end-to-end model, to ensure the effectiveness and stability of unsupervised domain adaptation, three critical factors are considered in our proposed optimization strategy, i.e., the sample amount of a target domain, dimension and batchsize of samples. Experimental results demonstrate that our model performs well on several standard benchmarks. Our ablation study shows that the sample amount of a target domain, the dimension and batchsize of samples indeed greatly impact Lipschitz-constraint-based methods' ability to handle large-scale datasets. Code is available at https://github.com/CuthbertCai/SRDA., Comment: Code is available at https://github.com/CuthbertCai/SRDA. Accepted by IEEE Transactions on Neural Networks and Learning Systems

Published
2019

41. Highly Efficient PtSn/Al 2 O 3 and PtSnZnCa/Al 2 O 3 Catalysts for Ethane Dehydrogenation: Influence of Catalyst Pretreatment Atmosphere.

Author

Podila, Seetharamulu, Al-Zahrani, Abdulrahim A., Daous, Muhammad A., and Alhumade, Hesham

Subjects
ALUMINUM oxide, PLATINUM, ATMOSPHERIC nitrogen, PLATINUM catalysts, CATALYSTS, X-ray photoelectron spectroscopy, DEHYDROGENATION
Abstract

Increased demand for ethylene has motivated direct ethane dehydrogenation over Pt-based catalysts. PtSn/γ-Al2O3 and PtSnZnCa/γ-Al2O3 catalysts were investigated with the aim of understanding the effect of the pretreatment environment on the state of dispersed Pt for ethane dehydrogenation. The catalysts were prepared by the impregnation method and pretreated in different environments like static air (SA), flowing air (FA), and nitrogen (N2) atmospheres. A comprehensive characterization of the catalysts was performed using Brunauer–Emmett–Teller (BET), X-ray diffraction (XRD), Temperature-Programmed Reduction (TPR), NH3 Temperature-Programmed Desorption (NH3-TPD), X-ray photoelectron spectroscopy (XPS), and Transmission Electron Microscopy (TEM) techniques. The results reveal that the PtSn on Al2O3 catalyst pretreated in the static air environment (PtSn-SA) exhibits 21% ethylene yield with 95% selectivity at 625 °C. XPS analysis found more platinum and tin on the catalyst surface after static air treatment. The overall acidity of the catalysts decreased after thermal treatment in static air. Elemental mapping demonstrated that Pt agglomeration was pronounced in catalysts calcined under flowing air and nitrogen. These factors are responsible for the enhanced activity of the PtSn-SA catalyst compared to the other catalysts. The addition of Zn and Ca to the PtSn catalysts increases the yield of the catalyst calcined in static air (PtSnZnCa-SA). The PtSnZnCa-SA catalyst showed the highest ethylene yield of 27% with 99% selectivity and highly stable activity at 625 °C for 10 h. [ABSTRACT FROM AUTHOR]

Published
2024
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