Mining of Single-Class by Active Learning for Semantic Segmentation
Hugues Lambert Emma Slade
Several Active Learning (AL) policies require retraining a target model
several times in order to identify the most informative samples and rarely
offer the option to focus on the acquisition of samples from underrepresented
classes. Here the Mining of Single-Class by Active Learning (MiSiCAL) paradigm
is introduced where an AL policy is constructed through deep reinforcement
learning and exploits quantity-accuracy correlations to build datasets on which
high-performance models can be trained with regards to specific classes.
MiSiCAL is especially helpful in the case of very large batch sizes since it
does not require repeated model training sessions as is common in other AL
methods. This is thanks to its ability to exploit fixed representations of the
candidate data points. We find that MiSiCAL is able to outperform a random
policy on 150 out of 171 COCO10k classes, while the strongest baseline only
outperforms random on 101 classes.