# Tightening Classification Boundaries in Open Set Domain Adaptation through Unknown Exploitation

arXiv: https://arxiv.org/abs/2309.08964 · HTML: https://mlsanity.com/paper/2309.08964/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 24 references that are in the map.
- First posted on arXiv 16 Sep 2023.

## Builds on (most cited first, 14 of 14)

- [Moment Matching for Multi-Source Domain Adaptation](https://mlsanity.com/paper/1812.01754/) (2018)
- [Deep Hashing Network for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1706.07522/) (2017)
- [Open-Set Recognition: a Good Closed-Set Classifier is All You Need?](https://mlsanity.com/paper/2110.06207/) (2021)
- [Recent Advances in Open Set Recognition: A Survey](https://mlsanity.com/paper/1811.08581/) (2018)
- [Universal Domain Adaptation through Self Supervision](https://mlsanity.com/paper/2002.07953/) (2020)
- [Adversarial Reciprocal Points Learning for Open Set Recognition](https://mlsanity.com/paper/2103.00953/) (2021)
- [Open Set Domain Adaptation by Backpropagation](https://mlsanity.com/paper/1804.10427/) (2018)
- [OVANet: One-vs-All Network for Universal Domain Adaptation](https://mlsanity.com/paper/2104.03344/) (2021)
- [On the Effectiveness of Image Rotation for Open Set Domain Adaptation](https://mlsanity.com/paper/2007.12360/) (2020)
- [Just DIAL: DomaIn Alignment Layers for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1702.06332/) (2017)
- [Distance-based Hyperspherical Classification for Multi-source Open-Set Domain Adaptation](https://mlsanity.com/paper/2107.02067/) (2021)
- [Improving Transferability of Domain Adaptation Networks Through Domain Alignment Layers](https://mlsanity.com/paper/2109.02693/) (2021)
- [Low-Budget Label Query through Domain Alignment Enforcement](https://mlsanity.com/paper/2001.00238/) (2020)
- [Open Set Domain Adaptation by Extreme Value Theory](https://mlsanity.com/paper/2101.02561/) (2020)

## Built on by (most cited first, 2 of 2)

- [Beyond the Known: Enhancing Open Set Domain Adaptation with Unknown Exploration](https://mlsanity.com/paper/2412.18105/) (2024)
- [E-MLNet: Enhanced Mutual Learning for Universal Domain Adaptation with Sample-Specific Weighting](https://mlsanity.com/paper/2509.09006/) (2025)

## Abstract (arXiv)

Convolutional Neural Networks (CNNs) have brought revolutionary advances to many research areas due to their capacity of learning from raw data. However, when those methods are applied to non-controllable environments, many different factors can degrade the model's expected performance, such as unlabeled datasets with different levels of domain shift and category shift. Particularly, when both issues occur at the same time, we tackle this challenging setup as Open Set Domain Adaptation (OSDA) problem. In general, existing OSDA approaches focus their efforts only on aligning known classes or, if they already extract possible negative instances, use them as a new category learned with supervision during the course of training. We propose a novel way to improve OSDA approaches by extracting a high-confidence set of unknown instances and using it as a hard constraint to tighten the classification boundaries of OSDA methods. Especially, we adopt a new loss constraint evaluated in three different means, (1) directly with the pristine negative instances; (2) with randomly transformed negatives using data augmentation techniques; and (3) with synthetically generated negatives containing adversarial features. We assessed all approaches in an extensive set of experiments based on OVANet, where we could observe consistent improvements for two public benchmarks, the Office-31 and Office-Home datasets, yielding absolute gains of up to 1.3% for both Accuracy and H-Score on Office-31 and 5.8% for Accuracy and 4.7% for H-Score on Office-Home.