# Collective Decision of One-vs-Rest Networks for Open Set Recognition

arXiv: https://arxiv.org/abs/2103.10230 · HTML: https://mlsanity.com/paper/2103.10230/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 58 references that are in the map.
- First posted on arXiv 18 Mar 2021.

## Builds on (most cited first, 27 of 27)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop](https://mlsanity.com/paper/1506.03365/) (2015)
- [Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks](https://mlsanity.com/paper/1706.02690/) (2017)
- [Towards Open Set Deep Networks](https://mlsanity.com/paper/1511.06233/) (2015)
- [Recent Advances in Open Set Recognition: A Survey](https://mlsanity.com/paper/1811.08581/) (2018)
- [Towards Open World Recognition](https://mlsanity.com/paper/1412.5687/) (2014)
- [Robustness via curvature regularization, and vice versa](https://mlsanity.com/paper/1811.09716/) (2018)
- [Generative OpenMax for Multi-Class Open Set Classification](https://mlsanity.com/paper/1707.07418/) (2017)
- [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)
- [DOC: Deep Open Classification of Text Documents](https://mlsanity.com/paper/1709.08716/) (2017)
- [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)
- [The Extreme Value Machine](https://mlsanity.com/paper/1506.06112/) (2015)
- [Open Set Domain Adaptation: Theoretical Bound and Algorithm](https://mlsanity.com/paper/1907.08375/) (2019)
- [Sparse Representation-based Open Set Recognition](https://mlsanity.com/paper/1705.02431/) (2017)
- [Deep CNN-based Multi-task Learning for Open-Set Recognition](https://mlsanity.com/paper/1903.03161/) (2019)
- [Collective decision for open set recognition](https://mlsanity.com/paper/1806.11258/) (2018)
- [From neural PCA to deep unsupervised learning](https://mlsanity.com/paper/1411.7783/) (2014)
- [One-vs-Rest Network-based Deep Probability Model for Open Set Recognition](https://mlsanity.com/paper/2004.08067/) (2020)
- [Open Set Intrusion Recognition for Fine-Grained Attack Categorization](https://mlsanity.com/paper/1703.02244/) (2017)
- [Denoising Autoencoders for Overgeneralization in Neural Networks](https://mlsanity.com/paper/1709.04762/) (2017)
- [Entropic one-class classifiers](https://mlsanity.com/paper/1407.7556/) (2014)
- [Be Careful What You Backpropagate: A Case For Linear Output Activations & Gradient Boosting](https://mlsanity.com/paper/1707.04199/) (2017)

## Built on by (most cited first, 10 of 10)

- [Curved Geometric Networks for Visual Anomaly Recognition](https://mlsanity.com/paper/2208.01188/) (2022)
- [A Survey on Open-Set Image Recognition](https://mlsanity.com/paper/2312.15571/) (2023)
- [A Comprehensive Review of Trends, Applications and Challenges In Out-of-Distribution Detection](https://mlsanity.com/paper/2209.12935/) (2022)
- [Teacher-Explorer-Student Learning: A Novel Learning Method for Open Set Recognition](https://mlsanity.com/paper/2103.12871/) (2021)
- [Understanding Open-Set Recognition by Jacobian Norm and Inter-Class Separation](https://mlsanity.com/paper/2209.11436/) (2022)
- [Unknown Health States Recognition With Collective Decision Based Deep Learning Networks In Predictive Maintenance Applications](https://mlsanity.com/paper/2310.17670/) (2023)
- [Domain Expansion and Boundary Growth for Open-Set Single-Source Domain Generalization](https://mlsanity.com/paper/2411.02920/) (2024)
- [Synthetic Unknown Class Learning for Learning Unknowns](https://mlsanity.com/paper/2111.08062/) (2021)
- [OpenAPMax: Abnormal Patterns-based Model for Real-World Alzheimer's Disease Diagnosis](https://mlsanity.com/paper/2307.00936/) (2023)
- [Graph Neural Network-Based Semi-Supervised Open-Set Fault Diagnosis for Marine Machinery Systems](https://mlsanity.com/paper/2511.01258/) (2025)

## Abstract (arXiv)

Unknown examples that are unseen during training often appear in real-world machine learning tasks, and an intelligent self-learning system should be able to distinguish between known and unknown examples. Accordingly, open set recognition (OSR), which addresses the problem of classifying knowns and identifying unknowns, has recently been highlighted. However, conventional deep neural networks using a softmax layer are vulnerable to overgeneralization, producing high confidence scores for unknowns. In this paper, we propose a simple OSR method based on the intuition that OSR performance can be maximized by setting strict and sophisticated decision boundaries that reject unknowns while maintaining satisfactory classification performance on knowns. For this purpose, a novel network structure is proposed, in which multiple one-vs-rest networks (OVRNs) follow a convolutional neural network feature extractor. Here, the OVRN is a simple feed-forward neural network that enhances the ability to reject nonmatches by learning class-specific discriminative features. Furthermore, the collective decision score is modeled by combining the multiple decisions reached by the OVRNs to alleviate overgeneralization. Extensive experiments were conducted on various datasets, and the experimental results showed that the proposed method performed significantly better than the state-of-the-art methods by effectively reducing overgeneralization.