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Senior Thesis 2024
Computer Science Department
School of Computer Science, Carnegie Mellon University
Robust Disaster Damage Assessment:
Leveraging Large Pretrained Models
Raashi Mohan
Senior Thesis
May 2024
Thesis Document
Robustness to distribution shifts is essential for utilizing machine learning models in real-world applications. Nevertheless, existing techniques that enhance the performance of machine learning in the presence of these shifts have primarily focused on shifts that are well-defined and uncomplicated – no method has proven successful in improving performance in the open-ended scenario considered in this study. Unfortunately, the de-facto standard of simply using existing data from previous related events to create models tailored towards specific tasks often falls short in several real-world situations, as these types of strategies are designed for conventional machine learning benchmarks, where a wealth of labels is available, and distribution shifts are absent. In light of these challenges, this thesis aims to devise novel approaches for effectively performing disaster assessment after natural disasters, while leveraging OpenAI's CLIP as a large pre-trained zero-shot backbone, with the goal of enhancing performance in the presence of distribution shifts.
15 pages
Advisor
Aditi Raghunathan
Amrith Setlur
Saurabh Garg
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