  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
na\"ively 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 semi-supervised learning methods to boost
performance.
