# Deep CNN-based Multi-task Learning for Open-Set Recognition

arXiv: https://arxiv.org/abs/1903.03161 · HTML: https://mlsanity.com/paper/1903.03161/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Mar 2019.

## Built on by (most cited first, 12 of 12)

- [Open-world Machine Learning: Applications, Challenges, and Opportunities](https://mlsanity.com/paper/2105.13448/) (2021)
- [Class-Specific Semantic Reconstruction for Open Set Recognition](https://mlsanity.com/paper/2207.02158/) (2022)
- [A Survey on Open Set Recognition](https://mlsanity.com/paper/2109.00893/) (2021)
- [A Unified Benchmark for the Unknown Detection Capability of Deep Neural Networks](https://mlsanity.com/paper/2112.00337/) (2021)
- [Collective Decision of One-vs-Rest Networks for Open Set Recognition](https://mlsanity.com/paper/2103.10230/) (2021)
- [Measuring Human Perception to Improve Open Set Recognition](https://mlsanity.com/paper/2209.03519/) (2022)
- [A Survey on Open-Set Image Recognition](https://mlsanity.com/paper/2312.15571/) (2023)
- [Adversarial Motorial Prototype Framework for Open Set Recognition](https://mlsanity.com/paper/2108.04225/) (2021)
- [ISSTAD: Incremental Self-Supervised Learning Based on Transformer for Anomaly Detection and Localization](https://mlsanity.com/paper/2303.17354/) (2023)
- [Open-set Recognition based on the Combination of Deep Learning and Ensemble Method for Detecting Unknown Traffic Scenarios](https://mlsanity.com/paper/2105.07635/) (2021)
- [Teacher-Explorer-Student Learning: A Novel Learning Method for Open Set Recognition](https://mlsanity.com/paper/2103.12871/) (2021)
- [Automatic Individual Identification of Patterned Solitary Species Based on Unlabeled Video Data](https://mlsanity.com/paper/2304.09657/) (2023)

## Abstract (arXiv)

We propose a novel deep convolutional neural network (CNN) based multi-task learning approach for open-set visual recognition. We combine a classifier network and a decoder network with a shared feature extractor network within a multi-task learning framework. We show that this approach results in better open-set recognition accuracy. In our approach, reconstruction errors from the decoder network are utilized for open-set rejection. In addition, we model the tail of the reconstruction error distribution from the known classes using the statistical Extreme Value Theory to improve the overall performance. Experiments on multiple image classification datasets are performed and it is shown that this method can perform significantly better than many competitive open set recognition algorithms available in the literature. The code will be made available at: github.com/otkupjnoz/mlosr.