mlsanityData to 7 Oct 2026

Difficulty-Aware Simulator for Open Set Recognition

arXiv 2207.10024 ↗First posted 20 Jul 2022cs.CVcited by 24

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Abstract (arXiv)

Open set recognition (OSR) assumes unknown instances appear out of the blue at the inference time. The main challenge of OSR is that the response of models for unknowns is totally unpredictable. Furthermore, the diversity of open set makes it harder since instances have different difficulty levels. Therefore, we present a novel framework, DIfficulty-Aware Simulator (DIAS), that generates fakes with diverse difficulty levels to simulate the real world. We first investigate fakes from generative adversarial network (GAN) in the classifier's viewpoint and observe that these are not severely challenging. This leads us to define the criteria for difficulty by regarding samples generated with GANs having moderate-difficulty. To produce hard-difficulty examples, we introduce Copycat, imitating the behavior of the classifier. Furthermore, moderate- and easy-difficulty samples are also yielded by our modified GAN and Copycat, respectively. As a result, DIAS outperforms state-of-the-art methods with both metrics of AUROC and F-score. Our code is available at https://github.com/wjun0830/Difficulty-Aware-Simulator.

Key facts. Cited by 24 papers in the map (data through 7 Oct 2026). Builds on 31 of its 46 references that are in the map. First posted on arXiv 20 Jul 2022.

Builds on

31 of 46 references.

References · 31

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2015Distilling the Knowledge in a Neural Networkcited by 10,076
  3. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,441
  4. 2016Densely Connected Convolutional Networkscited by 6,691
  5. 2022Generative Adversarial Networkscited by 5,603
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  10. 2014Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Imagescited by 774
  11. 2015Towards Open Set Deep Networkscited by 533
  12. 2020Generalized ODIN: Detecting Out-of-distribution Image without Learning from Out-of-distribution Datacited by 408
19 more
  1. 2017Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samplescited by 389
  2. 2021Distilling Knowledge via Knowledge Reviewcited by 285
  3. 2014Towards Open World Recognitioncited by 226
  4. 2017Generalization and Equilibrium in Generative Adversarial Nets (GANs)cited by 165
  5. 2017Generative OpenMax for Multi-Class Open Set Classificationcited by 157
  6. 2021Adversarial Reciprocal Points Learning for Open Set Recognitioncited by 153
  7. 2018Classification-Reconstruction Learning for Open-Set Recognitioncited by 146
  8. 2019C2AE: Class Conditioned Auto-Encoder for Open-set Recognitioncited by 142
  9. 2021OpenGAN: Open-Set Recognition via Open Data Generationcited by 135
  10. 2021Learning Placeholders for Open-Set Recognitioncited by 121
  11. 2020Learning Open Set Network with Discriminative Reciprocal Pointscited by 115
  12. 2017DOC: Deep Open Classification of Text Documentscited by 109
  13. 2020Hybrid Models for Open Set Recognitioncited by 109
  14. 2020Conditional Gaussian Distribution Learning for Open Set Recognitioncited by 104
  15. 2021Counterfactual Zero-Shot and Open-Set Visual Recognitioncited by 95
  16. 2021Towards Discovery and Attribution of Open-world GAN Generated Imagescited by 66
  17. 2017How hard can it be? Estimating the difficulty of visual search in an imagecited by 52
  18. 2017Sparse Representation-based Open Set Recognitioncited by 36
  19. 2021Conditional Variational Capsule Network for Open Set Recognitioncited by 17

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 10; 2024: 7; 2025: 4; 2026: 2.

24 papers.

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  1. 2023Query-Dependent Video Representation for Moment Retrieval and Highlight Detectioncited by 166
  2. 2023Progressive Open Space Expansion for Open-Set Model Attributioncited by 32
  3. 2024Gradient-Regularized Out-of-Distribution Detectioncited by 13
  4. 2023LMC: Large Model Collaboration with Cross-assessment for Training-Free Open-Set Object Recognitioncited by 10
  5. 2023A Survey on Open-Set Image Recognitioncited by 7
  6. 2024OSSAR: Towards Open-Set Surgical Activity Recognition in Robot-assisted Surgerycited by 7
  7. 2024Bidirectional Uncertainty-Based Active Learning for Open Set Annotationcited by 7
  8. 2024BOSC: A Backdoor-based Framework for Open Set Synthetic Image Attributioncited by 6
  9. 2023Domain-Generalized Face Anti-Spoofing with Unknown Attackscited by 4
  10. 2023Learning Large Margin Sparse Embeddings for Open Set Medical Diagnosiscited by 3
  11. 2023M-Tuning: Prompt Tuning with Mitigated Label Bias in Open-Set Scenarioscited by 2
  12. 2024OpenSlot: Mixed Open-Set Recognition with Object-Centric Learningcited by 2
12 more
  1. 2025UCDSC: Open Set UnCertainty aware Deep Simplex Classifier for Medical Image Datasetscited by 2
  2. 2024Dynamic Against Dynamic: An Open-set Self-learning Frameworkcited by 1
  3. 2025SphOR: A Representation Learning Perspective on Open-set Recognition for Identifying Unknown Classes in Deep Learning Modelscited by 1
  4. 2026DMDSC: A Dynamic-Margin Deep Simplex Classifier for Open-Set Recognition on Medical Image Datasetscited by 1
  5. 2022Spatial-Temporal Attention Network for Open-Set Fine-Grained Image Recognitioncited by 0
  6. 2023Complementary Frequency-Varying Awareness Network for Open-Set Fine-Grained Image Recognitioncited by 0
  7. 2023Recursive Counterfactual Deconfounding for Object Recognitioncited by 0
  8. 2023Towards Open-set Gesture Recognition via Feature Activation Enhancement and Orthogonal Prototype Learningcited by 0
  9. 2024OSR-ViT: A Simple and Modular Framework for Open-Set Object Detection and Discoverycited by 0
  10. 2025Boosting Open Set Recognition Performance through Modulated Representation Learningcited by 0
  11. 2025COSTARR: Consolidated Open Set Technique with Attenuation for Robust Recognitioncited by 0
  12. 2026Beyond the Simplex: Balanced Prototype Geometry for Scorer-Agnostic Open-Set Recognitioncited by 0