Abstract: Many unsupervised domain adaptation (UDA) methods have been proposed to bridge the domain gap by utilizing domain invariant information. Most approaches have chosen depth as such information and achieved remarkable successes. Despite their effectiveness, using depth as domain invariant information in UDA tasks may lead to multiple issues, such as excessively high extraction costs and difficulties in achieving a reliable prediction quality. As a result, we introduce Edge Learning based Domain Adaptation (ELDA), a framework which incorporates edge information into its training process to serve as a type of domain invariant information. Our experiments quantitatively and qualitatively demonstrate that the incorporation of edge information is indeed beneficial and effective, and enables ELDA to outperform the contemporary state-of-the-art methods on two commonly adopted benchmarks for semantic segmentation based UDA tasks.

Abstract: First radicals and second radicals are simultaneous deposited into a space defined by two adjacent lines of photoresists and an underlying layer. A portion of the first radicals and the second radicals combine to form a polymer layer on the layer in the center of the space, and substantially simultaneously, another portion of thee first radicals remove the underlying layer near the base of the photoresists. The first radicals may be fluorine-rich and the second radicals may be carbon-rich.

Abstract: First radicals and second radicals are simultaneous deposited into a space defined by two adjacent lines of photoresists and an underlying layer. A portion of the first radicals and the second radicals combine to form a polymer layer on the layer in the center of the space, and substantially simultaneously, another portion of thee first radicals remove the underlying layer near the base of the photoresists. The first radicals may be fluorine-rich and the second radicals may be carbon-rich.