We evaluate the effectiveness of semi-supervised learning (SSL) on a realistic benchmark where data exhibits considerable class imbalance and contains images from novel classes. Our benchmark consists of two fine-grained classification datasets obtained by sampling classes from the Aves and Fungi taxonomy. We find that recently proposed SSL methods provide significant benefits, and can effectively use out-of-class data to improve performance when deep networks are trained from scratch. Yet their performance pales in comparison to a transfer learning baseline, an alternative approach for learning from a few examples. Furthermore, in the transfer setting, while existing SSL methods provide improvements, the presence of out-of-class is often detrimental. In this setting, standard fine-tuning followed by distillation-based self-training is the most robust. Our work suggests that semi-supervised learning with experts on realistic datasets may require different strategies than those currently prevalent in the literature.

本篇研究评估了半监督学习在存在严重类别不平衡和含有新类别图像的真实数据集上的有效性。研究结果表明最近提出的半监督学习方法可以利用不同类别的数据提高性能，但相较于迁移学习仍然表现不足。此外，本文证明在迁移学习中，现有的半监督学习方法在存在不同类别数据时往往是有害的。因此，标准的微调加蒸馏自训练是最健壮的方法。这些研究结论说明，对真实数据集的半监督学习可能需要不同于文献中普遍使用的方法。

关于半监督学习在细粒度分类中的真实评估