  Semi-supervised Fine-Grained Recognition is a challenge task due to the
difficulty of data imbalance, high inter-class similarity and domain mismatch.
Recent years, this field has witnessed great progress and many methods has
gained great performance. However, these methods can hardly generalize to the
large-scale datasets, such as Semi-iNat, as they are prone to suffer from noise
in unlabeled data and the incompetence for learning features from imbalanced
fine-grained data. In this work, we propose Bilateral-Branch Self-Training
Framework (BiSTF), a simple yet effective framework to improve existing
semi-supervised learning methods on class-imbalanced and domain-shifted
fine-grained data. By adjusting the update frequency through stochastic epoch
update, BiSTF iteratively retrains a baseline SSL model with a labeled set
expanded by selectively adding pseudo-labeled samples from an unlabeled set,
where the distribution of pseudo-labeled samples are the same as the labeled
data. We show that BiSTF outperforms the existing state-of-the-art SSL
algorithm on Semi-iNat dataset.
