  For many practical problems and applications, it is not feasible to create a
vast and accurately labeled dataset, which restricts the application of deep
learning in many areas. Semi-supervised learning algorithms intend to improve
performance by also leveraging unlabeled data. This is very valuable for
2D-pose estimation task where data labeling requires substantial time and is
subject to noise. This work aims to investigate if semi-supervised learning
techniques can achieve acceptable performance level that makes using these
algorithms during training justifiable. To this end, a lightweight network
architecture is introduced and mean teacher, virtual adversarial training and
pseudo-labeling algorithms are evaluated on 2D-pose estimation for surgical
instruments. For the applicability of pseudo-labelling algorithm, we propose a
novel confidence measure, total variation. Experimental results show that
utilization of semi-supervised learning improves the performance on unseen
geometries drastically while maintaining high accuracy for seen geometries. For
RMIT benchmark, our lightweight architecture outperforms state-of-the-art with
supervised learning. For Endovis benchmark, pseudo-labelling algorithm improves
the supervised baseline achieving the new state-of-the-art performance.
