# Graph-Attention Network with Adversarial Domain Alignment for Robust Cross-Domain Facial Expression Recognition

arXiv: https://arxiv.org/abs/2512.00641 · HTML: https://mlsanity.com/paper/2512.00641/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 35 references that are in the map.
- First posted on arXiv 29 Nov 2025.

## Builds on (most cited first, 11 of 11)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Deep CORAL: Correlation Alignment for Deep Domain Adaptation](https://mlsanity.com/paper/1607.01719/) (2016)
- [Unbalanced minibatch Optimal Transport; applications to Domain Adaptation](https://mlsanity.com/paper/2103.03606/) (2021)
- [From Facial Expression Recognition to Interpersonal Relation Prediction](https://mlsanity.com/paper/1609.06426/) (2016)
- [Cross-Domain Facial Expression Recognition: A Unified Evaluation Benchmark and Adversarial Graph Learning](https://mlsanity.com/paper/2008.00923/) (2020)
- [A Deeper Look at Facial Expression Dataset Bias](https://mlsanity.com/paper/1904.11150/) (2019)
- [Shape Preserving Facial Landmarks with Graph Attention Networks](https://mlsanity.com/paper/2210.07233/) (2022)
- [A Survey on Facial Expression Recognition of Static and Dynamic Emotions](https://mlsanity.com/paper/2408.15777/) (2024)
- [Adaptive Global-Local Representation Learning and Selection for Cross-Domain Facial Expression Recognition](https://mlsanity.com/paper/2401.11085/) (2024)

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

- [MOSAIC: Multi-Domain Orthogonal Session Adaptive Intent Capture for Prescient Recommendations](https://mlsanity.com/paper/2604.10147/) (2026)
- [Facial Expression Recognition in the Deep Learning Era: A Systematic Multi-Criteria Review of Methods, Models, Datasets, Performance, Challenges, and Future Research Directions](https://mlsanity.com/paper/2606.08612/) (2026)

## Abstract (arXiv)

Cross-domain facial expression recognition (CD-FER) remains difficult due to severe domain shift between training and deployment data. We propose Graph-Attention Network with Adversarial Domain Alignment (GAT-ADA), a hybrid framework that couples a ResNet-50 as backbone with a batch-level Graph Attention Network (GAT) to model inter-sample relations under shift. Each mini-batch is cast as a sparse ring graph so that attention aggregates cross-sample cues that are informative for adaptation. To align distributions, GAT-ADA combines adversarial learning via a Gradient Reversal Layer (GRL) with statistical alignment using CORAL and MMD. GAT-ADA is evaluated under a standard unsupervised domain adaptation protocol: training on one labeled source (RAF-DB) and adapting to multiple unlabeled targets (CK+, JAFFE, SFEW 2.0, FER2013, and ExpW). GAT-ADA attains 74.39% mean cross-domain accuracy. On RAF-DB to FER2013, it reaches 98.0% accuracy, corresponding to approximately a 36-point improvement over the best baseline we re-implemented with the same backbone and preprocessing.