# Cross-Domain Similarity Learning for Face Recognition in Unseen Domains

arXiv: https://arxiv.org/abs/2103.07503 · HTML: https://mlsanity.com/paper/2103.07503/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 47 references that are in the map.
- First posted on arXiv 12 Mar 2021.

## Builds on (most cited first, 21 of 21)

- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Learning to Compare: Relation Network for Few-Shot Learning](https://mlsanity.com/paper/1711.06025/) (2017)
- [CosFace: Large Margin Cosine Loss for Deep Face Recognition](https://mlsanity.com/paper/1801.09414/) (2018)
- [Learning to Generalize: Meta-Learning for Domain Generalization](https://mlsanity.com/paper/1710.03463/) (2017)
- [Large-Scale Long-Tailed Recognition in an Open World](https://mlsanity.com/paper/1904.05160/) (2019)
- [MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition](https://mlsanity.com/paper/1607.08221/) (2016)
- [Learning Face Representation from Scratch](https://mlsanity.com/paper/1411.7923/) (2014)
- [Contrastive Adaptation Network for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1901.00976/) (2019)
- [Domain Generalization via Model-Agnostic Learning of Semantic Features](https://mlsanity.com/paper/1910.13580/) (2019)
- [How to train your MAML](https://mlsanity.com/paper/1810.09502/) (2018)
- [The MegaFace Benchmark: 1 Million Faces for Recognition at Scale](https://mlsanity.com/paper/1512.00596/) (2015)
- [Progressive Feature Alignment for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1811.08585/) (2018)
- [Feature-Critic Networks for Heterogeneous Domain Generalization](https://mlsanity.com/paper/1901.11448/) (2019)
- [Towards Universal Representation Learning for Deep Face Recognition](https://mlsanity.com/paper/2002.11841/) (2020)
- [The Devil of Face Recognition is in the Noise](https://mlsanity.com/paper/1807.11649/) (2018)
- [Learning Meta Face Recognition in Unseen Domains](https://mlsanity.com/paper/2003.07733/) (2020)
- [Deep Linear Discriminant Analysis](https://mlsanity.com/paper/1511.04707/) (2015)
- [Reconstruction-Based Disentanglement for Pose-invariant Face Recognition](https://mlsanity.com/paper/1702.03041/) (2017)
- [Improving Face Recognition by Clustering Unlabeled Faces in the Wild](https://mlsanity.com/paper/2007.06995/) (2020)

## Built on by (most cited first, 13 of 13)

- [Do Different Tracking Tasks Require Different Appearance Models?](https://mlsanity.com/paper/2107.02156/) (2021)
- [On Generalizing Beyond Domains in Cross-Domain Continual Learning](https://mlsanity.com/paper/2203.03970/) (2022)
- [FACE-AUDITOR: Data Auditing in Facial Recognition Systems](https://mlsanity.com/paper/2304.02782/) (2023)
- [Learning Semantic Segmentation from Multiple Datasets with Label Shifts](https://mlsanity.com/paper/2202.14030/) (2022)
- [SmartFRZ: An Efficient Training Framework using Attention-Based Layer Freezing](https://mlsanity.com/paper/2401.16720/) (2024)
- [Enhancing Adversarial Robustness for Deep Metric Learning](https://mlsanity.com/paper/2203.01439/) (2022)
- [Racial Bias within Face Recognition: A Survey](https://mlsanity.com/paper/2305.00817/) (2023)
- [Domain Generalization Guided by Gradient Signal to Noise Ratio of Parameters](https://mlsanity.com/paper/2310.07361/) (2023)
- [Domain-aware Triplet loss in Domain Generalization](https://mlsanity.com/paper/2303.01233/) (2023)
- [Domain Adaptive Detection of MAVs: A Benchmark and Noise Suppression Network](https://mlsanity.com/paper/2403.16669/) (2024)
- [Multiple data sources and domain generalization learning method for road surface defect classification](https://mlsanity.com/paper/2407.10197/) (2024)
- [Learning Mechanism Underlying NLP Pre-Training and Fine-Tuning](https://mlsanity.com/paper/2509.03407/) (2025)
- [A Survey of Face Recognition](https://mlsanity.com/paper/2212.13038/) (2022)

## Abstract (arXiv)

Face recognition models trained under the assumption of identical training and test distributions often suffer from poor generalization when faced with unknown variations, such as a novel ethnicity or unpredictable individual make-ups during test time. In this paper, we introduce a novel cross-domain metric learning loss, which we dub Cross-Domain Triplet (CDT) loss, to improve face recognition in unseen domains. The CDT loss encourages learning semantically meaningful features by enforcing compact feature clusters of identities from one domain, where the compactness is measured by underlying similarity metrics that belong to another training domain with different statistics. Intuitively, it discriminatively correlates explicit metrics derived from one domain, with triplet samples from another domain in a unified loss function to be minimized within a network, which leads to better alignment of the training domains. The network parameters are further enforced to learn generalized features under domain shift, in a model-agnostic learning pipeline. Unlike the recent work of Meta Face Recognition, our method does not require careful hard-pair sample mining and filtering strategy during training. Extensive experiments on various face recognition benchmarks show the superiority of our method in handling variations, compared to baseline and the state-of-the-art methods.