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Augmentation Consistency-guided Self-training for Source-free Domain Adaptive Semantic Segmentation
Viraj Prabhu · Shivam Khare · Deeksha Kartik · Judy Hoffman
Event URL: https://openreview.net/forum?id=tOCxfUebrlz »
We focus on source-free domain adaptation for semantic segmentation, wherein a source model must adapt itself to a new target domain given only unlabeled target data. We propose Augmentation Consistency-guided Self-training (AUGCO), an adaptation algorithm that uses the model's pixel-level predictive consistency across diverse, automatically generated views of each target image along with model confidence to identify reliable pixel predictions, and selectively self-trains on those, leading to state-of-the-art performance within a simple to implement and fast to converge approach.
Author Information
Viraj Prabhu (Georgia Tech)
I am a fourth year CS Ph.D. student at Georgia Tech, advised by Judy Hoffman. My research interests are in developing data-efficient and reliable computer vision systems that can be deployed in the real world. Specifically, I am interested in sample-efficient learning (particularly few-shot and active learning), adaptation across visual tasks and domains, and reliable and calibrated uncertainty estimation from deep neural networks.
Shivam Khare (Georgia Institute of Technology)
Deeksha Kartik (Georgia Institute of Technology)
Judy Hoffman (Georgia Institute of Technology)
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