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arXiv · 2103.17105

The GIST and RIST of Iterative Self-Training for Semi-Supervised Segmentation

Eu Wern Teh·Terrance DeVries·Brendan Duke·Ruowei Jiang·Parham Aarabi·Graham W. Taylor
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Abstract

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.

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Eu Wern Teh, Terrance DeVries, Brendan Duke, Ruowei Jiang, Parham Aarabi, Graham W. Taylor. 2021-03-31. The GIST and RIST of Iterative Self-Training for Semi-Supervised Segmentation. https://arxiv.org/abs/2103.17105

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