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Electrical Engineering and Computer Science (EECS)

Ph.D. Dissertation Defense

 

"Generalization and Adaptation of Deep Learning Models for Semantic Segmentation"
Xueqing Deng
Electrical Engineering and Computer Science

University of California, Merced

 

Abstract:

Thanks to the development of deep neural networks, a number of computer vision tasks have achieved great success. However, the focus has been mostly limited to benchmarks with regular scenes in a supervised training fashion. A deep learning model trained with perfect and ideal benchmark datasets can have difficulty when applied to real-world scenes where the data are captured under different settings, for example. This indicates the model has poor generalization capability. Problems also occur when a benchmark model is applied to a different real-world application than it was designed for and where the input data varies. Therefore, this dissertation seeks to improve model generalization and adaptation for the computer vision problem of semantic segmentation particularly for real-world applications.

 

Biography:

Xueqing Deng is a Ph.D. candidate in Electrical Engineering and Computer Science at the University of California, Merced. She received her B.S. degree from Sun Yat-Sen University, China in 2016. Her interests include generalization and adaptation problems for deep neural networks, and neural architecture search.

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