# Semi-Supervised Recognition under a Noisy and Fine-grained Dataset

arXiv: https://arxiv.org/abs/2006.10702 · HTML: https://mlsanity.com/paper/2006.10702/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jun 2020.

## Built on by (most cited first, 2 of 2)

- [The Semi-Supervised iNaturalist-Aves Challenge at FGVC7 Workshop](https://mlsanity.com/paper/2103.06937/) (2021)
- [Fine-Grained Adversarial Semi-supervised Learning](https://mlsanity.com/paper/2110.05848/) (2021)

## Abstract (arXiv)

Simi-Supervised Recognition Challenge-FGVC7 is a challenging fine-grained recognition competition. One of the difficulties of this competition is how to use unlabeled data. We adopted pseudo-tag data mining to increase the amount of training data. The other one is how to identify similar birds with a very small difference, especially those have a relatively tiny main-body in examples. We combined generic image recognition and fine-grained image recognition method to solve the problem. All generic image recognition models were training using PaddleClas . Using the combination of two different ways of deep recognition models, we finally won the third place in the competition.