@inproceedings{
schachtsiek2023class,
title={Class Balanced Dynamic Acquisition for Domain Adaptive Semantic Segmentation using Active Learning},
author={Marc Schachtsiek and Simone Rossi and Thomas Hannagan},
booktitle={NeurIPS 2023 Workshop on Adaptive Experimental Design and Active Learning in the Real World},
year={2023},
url={https://openreview.net/forum?id=wxpxPL3RkP}
}
Domain adaptive active learning is leading the charge in label-efficient training of neural networks. 
For semantic segmentation, state-of-the-art models jointly use two criteria of uncertainty and diversity to select training labels, combined with a pixel-wise acquisition strategy. 
However, we show that such methods currently suffer from a class imbalance issue which degrades their performance for larger active learning budgets. 
We then introduce Class Balanced Dynamic Acquisition (CBDA), a novel active learning method that mitigates this issue, especially in high-budget regimes. 
The more balanced labels increase minority class performance, which in turn allows the model to outperform the previous baseline by 0.6, 1.7, and 2.4 mIoU for budgets of 5%, 10%, and 20%, respectively. 
Additionally, the focus on minority classes leads to improvements of the minimum class performance of 0.5, 2.9, and 4.6 IoU respectively.
The top-performing model even exceeds the fully supervised baseline, showing that a more balanced label than the entire ground truth can be beneficial.
Class Balanced Dynamic Acquisition for Domain Adaptive Semantic Segmentation using Active Learning
Marc Schachtsiek Simone Rossi Thomas Hannagan