# Exploring Diverse Representations for Open Set Recognition

arXiv: https://arxiv.org/abs/2401.06521 · HTML: https://mlsanity.com/paper/2401.06521/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 26 references that are in the map.
- First posted on arXiv 12 Jan 2024.

## Builds on (most cited first, 16 of 16)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer](https://mlsanity.com/paper/1701.06538/) (2017)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks](https://mlsanity.com/paper/1610.02136/) (2016)
- [Shortcut Learning in Deep Neural Networks](https://mlsanity.com/paper/2004.07780/) (2020)
- [LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop](https://mlsanity.com/paper/1506.03365/) (2015)
- [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)
- [OpenGAN: Open-Set Recognition via Open Data Generation](https://mlsanity.com/paper/2104.02939/) (2021)
- [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)
- [Conditional Gaussian Distribution Learning for Open Set Recognition](https://mlsanity.com/paper/2003.08823/) (2020)
- [Deep Visual Attention Prediction](https://mlsanity.com/paper/1705.02544/) (2017)
- [Conditional Variational Capsule Network for Open Set Recognition](https://mlsanity.com/paper/2104.09159/) (2021)
- [Towards Open-Set Text Recognition via Label-to-Prototype Learning](https://mlsanity.com/paper/2203.05179/) (2022)

## Built on by (most cited first, 8 of 8)

- [BOSC: A Backdoor-based Framework for Open Set Synthetic Image Attribution](https://mlsanity.com/paper/2405.11491/) (2024)
- [Looking Beyond the Known: Towards a Data Discovery Guided Open-World Object Detection](https://mlsanity.com/paper/2510.00303/) (2025)
- [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)
- [Boosting Open Set Recognition Performance through Modulated Representation Learning](https://mlsanity.com/paper/2505.18137/) (2025)
- [SASep: Saliency-Aware Structured Separation of Geometry and Feature for Open Set Learning on Point Clouds](https://mlsanity.com/paper/2506.13224/) (2025)
- [Evolving from Unknown to Known: Retentive Angular Representation Learning for Incremental Open Set Recognition](https://mlsanity.com/paper/2509.06570/) (2025)
- [SmoothOperator: Enhancing Representations for Fine-grained Open-set Recognition via Modulated Label Smoothing](https://mlsanity.com/paper/2610.00851/) (2026)

## Abstract (arXiv)

Open set recognition (OSR) requires the model to classify samples that belong to closed sets while rejecting unknown samples during test. Currently, generative models often perform better than discriminative models in OSR, but recent studies show that generative models may be computationally infeasible or unstable on complex tasks. In this paper, we provide insights into OSR and find that learning supplementary representations can theoretically reduce the open space risk. Based on the analysis, we propose a new model, namely Multi-Expert Diverse Attention Fusion (MEDAF), that learns diverse representations in a discriminative way. MEDAF consists of multiple experts that are learned with an attention diversity regularization term to ensure the attention maps are mutually different. The logits learned by each expert are adaptively fused and used to identify the unknowns through the score function. We show that the differences in attention maps can lead to diverse representations so that the fused representations can well handle the open space. Extensive experiments are conducted on standard and OSR large-scale benchmarks. Results show that the proposed discriminative method can outperform existing generative models by up to 9.5% on AUROC and achieve new state-of-the-art performance with little computational cost. Our method can also seamlessly integrate existing classification models. Code is available at https://github.com/Vanixxz/MEDAF.