# On the Effectiveness of Image Rotation for Open Set Domain Adaptation

arXiv: https://arxiv.org/abs/2007.12360 · HTML: https://mlsanity.com/paper/2007.12360/

## Key facts

- Cited by 72 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jul 2020.

## Built on by (most cited first, 30 of 72)

- [Attracting and Dispersing: A Simple Approach for Source-free Domain Adaptation](https://mlsanity.com/paper/2205.04183/) (2022)
- [OVANet: One-vs-All Network for Universal Domain Adaptation](https://mlsanity.com/paper/2104.03344/) (2021)
- [Unified Optimal Transport Framework for Universal Domain Adaptation](https://mlsanity.com/paper/2210.17067/) (2022)
- [Domain Adaptation under Open Set Label Shift](https://mlsanity.com/paper/2207.13048/) (2022)
- [Upcycling Models under Domain and Category Shift](https://mlsanity.com/paper/2303.07110/) (2023)
- [Source-Free Progressive Graph Learning for Open-Set Domain Adaptation](https://mlsanity.com/paper/2202.06174/) (2022)
- [UMAD: Universal Model Adaptation under Domain and Category Shift](https://mlsanity.com/paper/2112.08553/) (2021)
- [Unknown-Aware Domain Adversarial Learning for Open-Set Domain Adaptation](https://mlsanity.com/paper/2206.07551/) (2022)
- [Your Data Is Not Perfect: Towards Cross-Domain Out-of-Distribution Detection in Class-Imbalanced Data](https://mlsanity.com/paper/2412.06284/) (2024)
- [Unknown Prompt, the only Lacuna: Unveiling CLIP's Potential for Open Domain Generalization](https://mlsanity.com/paper/2404.00710/) (2024)
- [Boosting Novel Category Discovery Over Domains with Soft Contrastive Learning and All-in-One Classifier](https://mlsanity.com/paper/2211.11262/) (2022)
- [AutoLabel: CLIP-based framework for Open-set Video Domain Adaptation](https://mlsanity.com/paper/2304.01110/) (2023)
- [Subsidiary Prototype Alignment for Universal Domain Adaptation](https://mlsanity.com/paper/2210.15909/) (2022)
- [Towards Multimodal Open-Set Domain Generalization and Adaptation through Self-supervision](https://mlsanity.com/paper/2407.01518/) (2024)
- [Universal Domain Adaptation via Compressive Attention Matching](https://mlsanity.com/paper/2304.11862/) (2023)
- [PracticalDG: Perturbation Distillation on Vision-Language Models for Hybrid Domain Generalization](https://mlsanity.com/paper/2404.09011/) (2024)
- [Towards Novel Target Discovery Through Open-Set Domain Adaptation](https://mlsanity.com/paper/2105.02432/) (2021)
- [Open-Set Domain Adaptation for Semantic Segmentation](https://mlsanity.com/paper/2405.19899/) (2024)
- [Generalized Domain Adaptation](https://mlsanity.com/paper/2106.01656/) (2021)
- [Distance-based Hyperspherical Classification for Multi-source Open-Set Domain Adaptation](https://mlsanity.com/paper/2107.02067/) (2021)
- [Open-Set Domain Adaptation with Visual-Language Foundation Models](https://mlsanity.com/paper/2307.16204/) (2023)
- [Conditional Extreme Value Theory for Open Set Video Domain Adaptation](https://mlsanity.com/paper/2109.00522/) (2021)
- [Progressively Select and Reject Pseudo-labelled Samples for Open-Set Domain Adaptation](https://mlsanity.com/paper/2110.12635/) (2021)
- [Imbalanced Open Set Domain Adaptation via Moving-threshold Estimation and Gradual Alignment](https://mlsanity.com/paper/2303.04393/) (2023)
- [Activate and Reject: Towards Safe Domain Generalization under Category Shift](https://mlsanity.com/paper/2310.04724/) (2023)
- [Learning Classifiers of Prototypes and Reciprocal Points for Universal Domain Adaptation](https://mlsanity.com/paper/2212.08355/) (2022)
- [DEVIAS: Learning Disentangled Video Representations of Action and Scene](https://mlsanity.com/paper/2312.00826/) (2023)
- [Prototypical Partial Optimal Transport for Universal Domain Adaptation](https://mlsanity.com/paper/2408.01089/) (2024)
- [Memory-Assisted Sub-Prototype Mining for Universal Domain Adaptation](https://mlsanity.com/paper/2310.05453/) (2023)
- [Recall and Refine: A Simple but Effective Source-free Open-set Domain Adaptation Framework](https://mlsanity.com/paper/2411.12558/) (2024)

## Abstract (arXiv)

Open Set Domain Adaptation (OSDA) bridges the domain gap between a labeled source domain and an unlabeled target domain, while also rejecting target classes that are not present in the source. To avoid negative transfer, OSDA can be tackled by first separating the known/unknown target samples and then aligning known target samples with the source data. We propose a novel method to addresses both these problems using the self-supervised task of rotation recognition. Moreover, we assess the performance with a new open set metric that properly balances the contribution of recognizing the known classes and rejecting the unknown samples. Comparative experiments with existing OSDA methods on the standard Office-31 and Office-Home benchmarks show that: (i) our method outperforms its competitors, (ii) reproducibility for this field is a crucial issue to tackle, (iii) our metric provides a reliable tool to allow fair open set evaluation.