School of Engineering Department of Electronic and Computer Engineering 135 Robust and Generalized Methods for Medical Image Analysis Supervisor: LI Xiaomeng / ECE Student: SUN Haosen / DSCT ZHOU Jialu / DSCT Course: UROP1100, Spring UROP2100, Summer UROP1100, Spring UROP2100, Summer The sum of the data outside the distribution is the challenge of the machine. This is because most of the learning algorithms so far are based on the assumptions of I.I.D. source or target data. However, under actual conditions, there are usually distribution differences between training sets and test sets. Domain adaptability is a method of solving such problems, but it is necessary to understand the test data. But in many cases, we know nothing about the test data. Therefore, in recent years, many people have begun to consider a more practical research scene called domain generalization. In this article, we have a more comprehensive understanding of the concept of domain summary by analyzing several articles about domain generalization and their respective innovation points. Robust and Generalized Methods for Medical Image Analysis Supervisor: LI Xiaomeng / ECE Student: YANG Lin / COMP Course: UROP2100, Fall Image restoration is an elementary tool for numerous low-level visionrelated tasks. It involves generating a high-quality clean image from a given low-quality degraded version. The degradation may occur during the picture capturing, transmitting, and preserving, resulting in different restoration tasks, such as denoising, deraining, dehazing, and deblurring. In this article, the development of image restoration tasks is reviewed, and two representative models will be focused on. One of them is an MLP-based model, which shows stateof-the-art performance on various restoration tasks, and the other one is a transformer-based model, which is all-in-one and degradation-blind. In the end, the future challenges of image restoration will be discussed.