# PMAL: Open Set Recognition via Robust Prototype Mining

arXiv: https://arxiv.org/abs/2203.08569 · HTML: https://mlsanity.com/paper/2203.08569/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 31 references that are in the map.
- First posted on arXiv 16 Mar 2022.

## Builds on (most cited first, 19 of 19)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Large-Scale Long-Tailed Recognition in an Open World](https://mlsanity.com/paper/1904.05160/) (2019)
- [Towards Open Set Deep Networks](https://mlsanity.com/paper/1511.06233/) (2015)
- [Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding](https://mlsanity.com/paper/1511.02680/) (2015)
- [Probabilistic Face Embeddings](https://mlsanity.com/paper/1904.09658/) (2019)
- [Generative OpenMax for Multi-Class Open Set Classification](https://mlsanity.com/paper/1707.07418/) (2017)
- [Adversarial Reciprocal Points Learning for Open Set Recognition](https://mlsanity.com/paper/2103.00953/) (2021)
- [Classification-Reconstruction Learning for Open-Set Recognition](https://mlsanity.com/paper/1812.04246/) (2018)
- [C2AE: Class Conditioned Auto-Encoder for Open-set Recognition](https://mlsanity.com/paper/1904.01198/) (2019)
- [Data Uncertainty Learning in Face Recognition](https://mlsanity.com/paper/2003.11339/) (2020)
- [Robust Classification with Convolutional Prototype Learning](https://mlsanity.com/paper/1805.03438/) (2018)
- [Learning Placeholders for Open-Set Recognition](https://mlsanity.com/paper/2103.15086/) (2021)
- [Learning Open Set Network with Discriminative Reciprocal Points](https://mlsanity.com/paper/2011.00178/) (2020)
- [Hybrid Models for Open Set Recognition](https://mlsanity.com/paper/2003.12506/) (2020)
- [Conditional Gaussian Distribution Learning for Open Set Recognition](https://mlsanity.com/paper/2003.08823/) (2020)

## Built on by (most cited first, 27 of 27)

- [Entropic Open-set Active Learning](https://mlsanity.com/paper/2312.14126/) (2023)
- [Navigating Open Set Scenarios for Skeleton-based Action Recognition](https://mlsanity.com/paper/2312.06330/) (2023)
- [Learning Adversarial Semantic Embeddings for Zero-Shot Recognition in Open Worlds](https://mlsanity.com/paper/2307.03416/) (2023)
- [LMC: Large Model Collaboration with Cross-assessment for Training-Free Open-Set Object Recognition](https://mlsanity.com/paper/2309.12780/) (2023)
- [SMART-Vision: Survey of Modern Action Recognition Techniques in Vision](https://mlsanity.com/paper/2501.13066/) (2025)
- [A Survey on Open-Set Image Recognition](https://mlsanity.com/paper/2312.15571/) (2023)
- [Managing the unknown: a survey on Open Set Recognition and tangential areas](https://mlsanity.com/paper/2312.08785/) (2023)
- [Learning Large Margin Sparse Embeddings for Open Set Medical Diagnosis](https://mlsanity.com/paper/2307.04541/) (2023)
- [All Beings Are Equal in Open Set Recognition](https://mlsanity.com/paper/2401.17654/) (2024)
- [ProtoConNet: Prototypical Augmentation and Alignment for Open-Set Few-Shot Image Classification](https://mlsanity.com/paper/2507.11845/) (2025)
- [M-Tuning: Prompt Tuning with Mitigated Label Bias in Open-Set Scenarios](https://mlsanity.com/paper/2303.05122/) (2023)
- [Latent Space Energy-based Model for Fine-grained Open Set Recognition](https://mlsanity.com/paper/2309.10711/) (2023)
- [BSDP: Brain-inspired Streaming Dual-level Perturbations for Online Open World Object Detection](https://mlsanity.com/paper/2403.02637/) (2024)
- [A Unified Evaluation Framework for Novelty Detection and Accommodation in NLP with an Instantiation in Authorship Attribution](https://mlsanity.com/paper/2305.05079/) (2023)
- [Open-Set RF Fingerprinting via Improved Prototype Learning](https://mlsanity.com/paper/2306.13895/) (2023)
- [Prototype Fission: Closing Set for Robust Open-set Semi-supervised Learning](https://mlsanity.com/paper/2308.15575/) (2023)
- [Dynamic Against Dynamic: An Open-set Self-learning Framework](https://mlsanity.com/paper/2404.17830/) (2024)
- [Augmented Contrastive Clustering with Uncertainty-Aware Prototyping for Time Series Test Time Adaptation](https://mlsanity.com/paper/2501.01472/) (2025)
- [Spatial-Temporal Attention Network for Open-Set Fine-Grained Image Recognition](https://mlsanity.com/paper/2211.13940/) (2022)
- [Partial-label Learning with Mixed Closed-set and Open-set Out-of-candidate Examples](https://mlsanity.com/paper/2307.00553/) (2023)
- [Complementary Frequency-Varying Awareness Network for Open-Set Fine-Grained Image Recognition](https://mlsanity.com/paper/2307.07214/) (2023)
- [Recursive Counterfactual Deconfounding for Object Recognition](https://mlsanity.com/paper/2309.13924/) (2023)
- [Multi-method Integration with Confidence-based Weighting for Zero-shot Image Classification](https://mlsanity.com/paper/2405.02155/) (2024)
- [Boosting Open Set Recognition Performance through Modulated Representation Learning](https://mlsanity.com/paper/2505.18137/) (2025)
- [COSTARR: Consolidated Open Set Technique with Attenuation for Robust Recognition](https://mlsanity.com/paper/2508.01087/) (2025)
- [Evolving from Unknown to Known: Retentive Angular Representation Learning for Incremental Open Set Recognition](https://mlsanity.com/paper/2509.06570/) (2025)
- [Exploring Dualistic Meta-Learning to Enhance Domain Generalization in Open Set Scenarios](https://mlsanity.com/paper/2606.23758/) (2026)

## Abstract (arXiv)

Open Set Recognition (OSR) has been an emerging topic. Besides recognizing predefined classes, the system needs to reject the unknowns. Prototype learning is a potential manner to handle the problem, as its ability to improve intra-class compactness of representations is much needed in discrimination between the known and the unknowns. In this work, we propose a novel Prototype Mining And Learning (PMAL) framework. It has a prototype mining mechanism before the phase of optimizing embedding space, explicitly considering two crucial properties, namely high-quality and diversity of the prototype set. Concretely, a set of high-quality candidates are firstly extracted from training samples based on data uncertainty learning, avoiding the interference from unexpected noise. Considering the multifarious appearance of objects even in a single category, a diversity-based strategy for prototype set filtering is proposed. Accordingly, the embedding space can be better optimized to discriminate therein the predefined classes and between known and unknowns. Extensive experiments verify the two good characteristics (i.e., high-quality and diversity) embraced in prototype mining, and show the remarkable performance of the proposed framework compared to state-of-the-arts.