  We consider the task of semi-supervised semantic segmentation, where we aim
to produce pixel-wise semantic object masks given only a small number of
human-labeled training examples. We focus on iterative self-training methods in
which we explore the behavior of self-training over multiple refinement stages.
We show that iterative self-training leads to performance degradation if done
naively with a fixed ratio of human-labeled to pseudo-labeled training
examples. We propose Greedy Iterative Self-Training (GIST) and Random Iterative
Self-Training (RIST) strategies that alternate between training on either
human-labeled data or pseudo-labeled data at each refinement stage, resulting
in a performance boost rather than degradation. We further show that GIST and
RIST can be combined with existing SOTA methods to boost performance, yielding
new SOTA results in Pascal VOC 2012 and Cityscapes dataset across five out of
six subsets.
