# Open-Category Classification by Adversarial Sample Generation

arXiv: https://arxiv.org/abs/1705.08722 · HTML: https://mlsanity.com/paper/1705.08722/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 May 2017.

## Built on by (most cited first, 24 of 24)

- [Towards Open Vocabulary Learning: A Survey](https://mlsanity.com/paper/2306.15880/) (2023)
- [Learning Placeholders for Open-Set Recognition](https://mlsanity.com/paper/2103.15086/) (2021)
- [A Survey on Open Set Recognition](https://mlsanity.com/paper/2109.00893/) (2021)
- [Graph Learning under Distribution Shifts: A Comprehensive Survey on Domain Adaptation, Out-of-distribution, and Continual Learning](https://mlsanity.com/paper/2402.16374/) (2024)
- [Learning Discriminative Representations and Decision Boundaries for Open Intent Detection](https://mlsanity.com/paper/2203.05823/) (2022)
- [Open World Object Detection: A Survey](https://mlsanity.com/paper/2410.11301/) (2024)
- [Workflow Discovery from Dialogues in the Low Data Regime](https://mlsanity.com/paper/2205.11690/) (2022)
- [Learning Adversarial Semantic Embeddings for Zero-Shot Recognition in Open Worlds](https://mlsanity.com/paper/2307.03416/) (2023)
- [Word-level Human Interpretable Scoring Mechanism for Novel Text Detection Using Tsetlin Machines](https://mlsanity.com/paper/2105.04708/) (2021)
- [Large-Scale Open-Set Classification Protocols for ImageNet](https://mlsanity.com/paper/2210.06789/) (2022)
- [G2Pxy: Generative Open-Set Node Classification on Graphs with Proxy Unknowns](https://mlsanity.com/paper/2308.05463/) (2023)
- [Adversarial Motorial Prototype Framework for Open Set Recognition](https://mlsanity.com/paper/2108.04225/) (2021)
- [Effective Open Intent Classification with K-center Contrastive Learning and Adjustable Decision Boundary](https://mlsanity.com/paper/2304.10220/) (2023)
- [Unsupervised detection and open-set classification of fast-ramped flexibility activation events](https://mlsanity.com/paper/2111.02174/) (2021)
- [M-Tuning: Prompt Tuning with Mitigated Label Bias in Open-Set Scenarios](https://mlsanity.com/paper/2303.05122/) (2023)
- [ROGPL: Robust Open-Set Graph Learning via Region-Based Prototype Learning](https://mlsanity.com/paper/2402.18495/) (2024)
- [Unsupervised Learning of Multi-level Structures for Anomaly Detection](https://mlsanity.com/paper/2104.12102/) (2021)
- [Enhancing Open-World Bacterial Raman Spectra Identification by Feature Regularization for Improved Resilience against Unknown Classes](https://mlsanity.com/paper/2310.13723/) (2023)
- [Taking Class Imbalance Into Account in Open Set Recognition Evaluation](https://mlsanity.com/paper/2402.06331/) (2024)
- [Feature Decoupling in Self-supervised Representation Learning for Open Set Recognition](https://mlsanity.com/paper/2209.14385/) (2022)
- [OpenIncrement: A Unified Framework for Open Set Recognition and Deep Class-Incremental Learning](https://mlsanity.com/paper/2310.03848/) (2023)
- [Know Yourself Better: Diverse Object-Related Features Improve Open Set Recognition](https://mlsanity.com/paper/2404.10370/) (2024)
- [Informed Mixing -- Improving Open Set Recognition via Attribution-based Augmentation](https://mlsanity.com/paper/2505.12803/) (2025)
- [Empirical Evidences for the Effects of Feature Diversity in Open Set Recognition and Continual Learning](https://mlsanity.com/paper/2508.13005/) (2025)

## Abstract (arXiv)

In real-world classification tasks, it is difficult to collect training samples from all possible categories of the environment. Therefore, when an instance of an unseen class appears in the prediction stage, a robust classifier should be able to tell that it is from an unseen class, instead of classifying it to be any known category. In this paper, adopting the idea of adversarial learning, we propose the ASG framework for open-category classification. ASG generates positive and negative samples of seen categories in the unsupervised manner via an adversarial learning strategy. With the generated samples, ASG then learns to tell seen from unseen in the supervised manner. Experiments performed on several datasets show the effectiveness of ASG.