# S2OSC: A Holistic Semi-Supervised Approach for Open Set Classification

arXiv: https://arxiv.org/abs/2008.04662 · HTML: https://mlsanity.com/paper/2008.04662/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Aug 2020.

## Built on by (most cited first, 15 of 15)

- [Robust Semi-supervised Learning by Wisely Leveraging Open-set Data](https://mlsanity.com/paper/2405.06979/) (2024)
- [PG-LBO: Enhancing High-Dimensional Bayesian Optimization with Pseudo-Label and Gaussian Process Guidance](https://mlsanity.com/paper/2312.16983/) (2023)
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- [Unlocking the Power of Open Set : A New Perspective for Open-Set Noisy Label Learning](https://mlsanity.com/paper/2305.04203/) (2023)
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- [Safe Semi-Supervised Contrastive Learning Using In-Distribution Data as Positive Examples](https://mlsanity.com/paper/2408.01872/) (2024)
- [Solution for OOD-CV Workshop SSB Challenge 2024 (Open-Set Recognition Track)](https://mlsanity.com/paper/2409.20277/) (2024)
- [Graph Neural Network-Based Semi-Supervised Open-Set Fault Diagnosis for Marine Machinery Systems](https://mlsanity.com/paper/2511.01258/) (2025)
- [Geometric Gradient Rectification for Safe Open-Set Semi-Supervised Learning](https://mlsanity.com/paper/2606.26973/) (2026)

## Abstract (arXiv)

Open set classification (OSC) tackles the problem of determining whether the data are in-class or out-of-class during inference, when only provided with a set of in-class examples at training time. Traditional OSC methods usually train discriminative or generative models with in-class data, then utilize the pre-trained models to classify test data directly. However, these methods always suffer from embedding confusion problem, i.e., partial out-of-class instances are mixed with in-class ones of similar semantics, making it difficult to classify. To solve this problem, we unify semi-supervised learning to develop a novel OSC algorithm, S2OSC, that incorporates out-of-class instances filtering and model re-training in a transductive manner. In detail, given a pool of newly coming test data, S2OSC firstly filters distinct out-of-class instances using the pre-trained model, and annotates super-class for them. Then, S2OSC trains a holistic classification model by combing in-class and out-of-class labeled data and remaining unlabeled test data in semi-supervised paradigm, which also integrates pre-trained model for knowledge distillation to further separate mixed instances. Despite its simplicity, the experimental results show that S2OSC achieves state-of-the-art performance across a variety of OSC tasks, including 85.4% of F1 on CIFAR-10 with only 300 pseudo-labels. We also demonstrate how S2OSC can be expanded to incremental OSC setting effectively with streaming data.