# Towards Robust Pattern Recognition: A Review

arXiv: https://arxiv.org/abs/2006.06976 · HTML: https://mlsanity.com/paper/2006.06976/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jun 2020.

## Built on by (most cited first, 30 of 31)

- [A Review on Explainability in Multimodal Deep Neural Nets](https://mlsanity.com/paper/2105.07878/) (2021)
- [A Survey of Robust Adversarial Training in Pattern Recognition: Fundamental, Theory, and Methodologies](https://mlsanity.com/paper/2203.14046/) (2022)
- [OpenMix: Exploring Outlier Samples for Misclassification Detection](https://mlsanity.com/paper/2303.17093/) (2023)
- [Multi-View Correlation Distillation for Incremental Object Detection](https://mlsanity.com/paper/2107.01787/) (2021)
- [LTD: Low Temperature Distillation for Gradient Masking-free Adversarial Training](https://mlsanity.com/paper/2111.02331/) (2021)
- [Federated Continual Instruction Tuning](https://mlsanity.com/paper/2503.12897/) (2025)
- [Continual Learning for Generative AI: From LLMs to MLLMs and Beyond](https://mlsanity.com/paper/2506.13045/) (2025)
- [Deep Learning for Scene Classification: A Survey](https://mlsanity.com/paper/2101.10531/) (2021)
- [A Survey of Orthogonal Moments for Image Representation: Theory, Implementation, and Evaluation](https://mlsanity.com/paper/2103.14799/) (2021)
- [A Principled Design of Image Representation: Towards Forensic Tasks](https://mlsanity.com/paper/2203.00913/) (2022)
- [Open-set Text Recognition via Character-Context Decoupling](https://mlsanity.com/paper/2204.05535/) (2022)
- [Adversarial Parameter Attack on Deep Neural Networks](https://mlsanity.com/paper/2203.10502/) (2022)
- [Typicalness-Aware Learning for Failure Detection](https://mlsanity.com/paper/2411.01981/) (2024)
- [An End-to-End Computer Vision Methodology for Quantitative Metallography](https://mlsanity.com/paper/2104.11159/) (2021)
- [Learning Large Margin Sparse Embeddings for Open Set Medical Diagnosis](https://mlsanity.com/paper/2307.04541/) (2023)
- [Robust and Information-theoretically Safe Bias Classifier against Adversarial Attacks](https://mlsanity.com/paper/2111.04404/) (2021)
- [Mutual Information Learned Classifiers: an Information-theoretic Viewpoint of Training Deep Learning Classification Systems](https://mlsanity.com/paper/2209.10058/) (2022)
- [Representing Noisy Image Without Denoising](https://mlsanity.com/paper/2301.07409/) (2023)
- [Improving Taxonomic Image-based Out-of-distribution Detection With DNA Barcodes](https://mlsanity.com/paper/2406.18999/) (2024)
- [A Robust Classification-autoencoder to Defend Outliers and Adversaries](https://mlsanity.com/paper/2106.15927/) (2021)
- [Selective Probabilistic Classifier Based on Hypothesis Testing](https://mlsanity.com/paper/2105.03876/) (2021)
- [A Mathematical Foundation for Robust Machine Learning based on Bias-Variance Trade-off](https://mlsanity.com/paper/2106.05522/) (2021)
- [Mutual Information Learned Classifiers: an Information-theoretic Viewpoint of Training Deep Learning Classification Systems](https://mlsanity.com/paper/2210.01000/) (2022)
- [License Plate Recognition Based On Multi-Angle View Model](https://mlsanity.com/paper/2309.12972/) (2023)
- [YOLOOC: YOLO-based Open-Class Incremental Object Detection with Novel Class Discovery](https://mlsanity.com/paper/2404.00257/) (2024)
- [Robust Capped lp-Norm Support Vector Ordinal Regression](https://mlsanity.com/paper/2404.16616/) (2024)
- [Identifying Information from Observations with Uncertainty and Novelty](https://mlsanity.com/paper/2501.09331/) (2025)
- [Semi-parametric Memory Consolidation: Towards Brain-like Deep Continual Learning](https://mlsanity.com/paper/2504.14727/) (2025)
- [Insect-Wing Structured Microfluidic System for Reservoir Computing](https://mlsanity.com/paper/2508.10915/) (2025)
- [FuDU: A Fuzzy Dual-dimensional Uncertainty Framework for Streaming Active Learning in Industrial Defect Detection](https://mlsanity.com/paper/2609.02212/) (2026)

## Abstract (arXiv)

The accuracies for many pattern recognition tasks have increased rapidly year by year, achieving or even outperforming human performance. From the perspective of accuracy, pattern recognition seems to be a nearly-solved problem. However, once launched in real applications, the high-accuracy pattern recognition systems may become unstable and unreliable, due to the lack of robustness in open and changing environments. In this paper, we present a comprehensive review of research towards robust pattern recognition from the perspective of breaking three basic and implicit assumptions: closed-world assumption, independent and identically distributed assumption, and clean and big data assumption, which form the foundation of most pattern recognition models. Actually, our brain is robust at learning concepts continually and incrementally, in complex, open and changing environments, with different contexts, modalities and tasks, by showing only a few examples, under weak or noisy supervision. These are the major differences between human intelligence and machine intelligence, which are closely related to the above three assumptions. After witnessing the significant progress in accuracy improvement nowadays, this review paper will enable us to analyze the shortcomings and limitations of current methods and identify future research directions for robust pattern recognition.