# Uncertainty-guided Contrastive Learning for Single Source Domain Generalisation

arXiv: https://arxiv.org/abs/2403.07514 · HTML: https://mlsanity.com/paper/2403.07514/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 22 references that are in the map.
- First posted on arXiv 12 Mar 2024.

## Builds on (most cited first, 17 of 17)

- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Wide Residual Networks](https://mlsanity.com/paper/1605.07146/) (2016)
- [Benchmarking Neural Network Robustness to Common Corruptions and Perturbations](https://mlsanity.com/paper/1903.12261/) (2019)
- [Exploring Simple Siamese Representation Learning](https://mlsanity.com/paper/2011.10566/) (2020)
- [Deeper, Broader and Artier Domain Generalization](https://mlsanity.com/paper/1710.03077/) (2017)
- [Generalizing to Unseen Domains via Adversarial Data Augmentation](https://mlsanity.com/paper/1805.12018/) (2018)
- [Domain Generalization by Solving Jigsaw Puzzles](https://mlsanity.com/paper/1903.06864/) (2019)
- [Learning to Learn Single Domain Generalization](https://mlsanity.com/paper/2003.13216/) (2020)
- [Learning to Diversify for Single Domain Generalization](https://mlsanity.com/paper/2108.11726/) (2021)
- [Robust and Generalizable Visual Representation Learning via Random Convolutions](https://mlsanity.com/paper/2007.13003/) (2020)
- [Exact Feature Distribution Matching for Arbitrary Style Transfer and Domain Generalization](https://mlsanity.com/paper/2203.07740/) (2022)
- [Pre-training without Natural Images](https://mlsanity.com/paper/2101.08515/) (2021)
- [Progressive Domain Expansion Network for Single Domain Generalization](https://mlsanity.com/paper/2103.16050/) (2021)
- [Uncertainty-guided Model Generalization to Unseen Domains](https://mlsanity.com/paper/2103.07531/) (2021)
- [Out-of-Domain Generalization from a Single Source: An Uncertainty Quantification Approach](https://mlsanity.com/paper/2108.02888/) (2021)
- [Improving Diversity with Adversarially Learned Transformations for Domain Generalization](https://mlsanity.com/paper/2206.07736/) (2022)

## Built on by (most cited first, 14 of 14)

- [7th ABAW Competition: Multi-Task Learning and Compound Expression Recognition](https://mlsanity.com/paper/2407.03835/) (2024)
- [Behaviour4All: in-the-wild Facial Behaviour Analysis Toolkit](https://mlsanity.com/paper/2409.17717/) (2024)
- [SAM2CLIP2SAM: Vision Language Model for Segmentation of 3D CT Scans for Covid-19 Detection](https://mlsanity.com/paper/2407.15728/) (2024)
- [DVD: A Comprehensive Dataset for Advancing Violence Detection in Real-World Scenarios](https://mlsanity.com/paper/2506.05372/) (2025)
- [Ensuring UAV Safety: A Vision-only and Real-time Framework for Collision Avoidance Through Object Detection, Tracking, and Distance Estimation](https://mlsanity.com/paper/2405.06749/) (2024)
- [Can Machine Learning Assist in Diagnosis of Primary Immune Thrombocytopenia? A feasibility study](https://mlsanity.com/paper/2405.20562/) (2024)
- [Rethinking Affect Analysis: A Protocol for Ensuring Fairness and Consistency](https://mlsanity.com/paper/2408.02164/) (2024)
- [Using Prompts to Guide Large Language Models in Imitating a Real Person's Language Style](https://mlsanity.com/paper/2410.03848/) (2024)
- [Robust Facial Reactions Generation: An Emotion-Aware Framework with Modality Compensation](https://mlsanity.com/paper/2407.15798/) (2024)
- [Crossing the Species Divide: Transfer Learning from Speech to Animal Sounds](https://mlsanity.com/paper/2509.04166/) (2025)
- [Common Corruptions for Enhancing and Evaluating Robustness in Air-to-Air Visual Object Detection](https://mlsanity.com/paper/2405.06765/) (2024)
- [Complex Style Image Transformations for Domain Generalization in Medical Images](https://mlsanity.com/paper/2406.00298/) (2024)
- [Emotion Detection through Body Gesture and Face](https://mlsanity.com/paper/2407.09913/) (2024)
- [An Entropy-Guided Curriculum Learning Strategy for Data-Efficient Acoustic Scene Classification under Domain Shift](https://mlsanity.com/paper/2509.11168/) (2025)

## Abstract (arXiv)

In the context of single domain generalisation, the objective is for models that have been exclusively trained on data from a single domain to demonstrate strong performance when confronted with various unfamiliar domains. In this paper, we introduce a novel model referred to as Contrastive Uncertainty Domain Generalisation Network (CUDGNet). The key idea is to augment the source capacity in both input and label spaces through the fictitious domain generator and jointly learn the domain invariant representation of each class through contrastive learning. Extensive experiments on two Single Source Domain Generalisation (SSDG) datasets demonstrate the effectiveness of our approach, which surpasses the state-of-the-art single-DG methods by up to 7.08%. Our method also provides efficient uncertainty estimation at inference time from a single forward pass through the generator subnetwork.