Date: July 2nd, 2024
Time: 2:30pm - 4:30pm ET
Location: CODA C1115 Druid Hills
Zoom Link: https://gatech.zoom.us/j/96316092795
Prithvijit Chattopadhyay
Computer Science PhD Student
Interactive Computing
Georgia Institute of Technology
Committee
Abstract
Progress in computer vision has been driven by models trained on large amounts of exemplar data for different tasks. These exemplar data sources intend to capture task-specific information and instance-level variations that a trained model will likely encounter in the wild. However, for conditions where curating lots of labeled real-world data is prohibitively expensive, synthetic data can serve as a cost-effective alternative. Synthetic data sources offer a few key benefits: fast access to labeled task-specific data at scale, labels across varying task complexities, and curation of labeled data across diverse conditions in a controlled manner.
In this thesis defense, I will focus on how “controlled variations” in synthetic data can be used to develop robust and reliable vision models. Controlled variations refer to intentional, systematic modifications to synthetic data, designed to either explore specific aspects of model behavior or improve model transfer across distributions. First, I will discuss RobustNav and SkyScenes, which apply controlled variations internally at the data-engine (simulator) stage to create diverse instances to systematically investigate the robustness of trained vision models. Next, I will discuss PASTA and AUGCAL, which apply controlled variations externally as data augmentations, guided by observed distributional discrepancies between synthetic and real data, to develop robust and reliable models.
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