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: A probe pin cleaning pad including a foam layer, a cleaning layer, and a polishing layer is provided. The cleaning layer is disposed between the foam layer and the polishing layer. A cleaning method for a probe pin is also provided.

Abstract: A model generating apparatus and method are provided. The apparatus receives a plurality of sample images. The apparatus generates a plurality of adversarial samples corresponding to the sample images. The apparatus inputs the sample images and the adversarial samples respectively to a first encoder and a second encoder in a self-supervised neural network to generate a plurality of first feature extractions and a plurality of second feature extractions. The apparatus calculates a similarity of each of the first feature extractions and the second feature extractions to train the self-supervised neural network. The apparatus generates a task model based on the first encoder and a plurality of labeled data.