A Realistic Evaluation of Semi-Supervised Learning for Fine-Grained Classification

• URL: http://arxiv.org/abs/2104.00679v1
• Date: Thu, 1 Apr 2021 17:59:41 GMT
• Title: A Realistic Evaluation of Semi-Supervised Learning for Fine-Grained Classification
• Authors: Jong-Chyi Su and Zezhou Cheng and Subhransu Maji
• Abstract summary: 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. Our work suggests that semi-supervised learning with experts on realistic datasets may require different strategies than those currently prevalent in the literature.
• Score: 38.68079253627819
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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