Skip to content
Log in
Review Open access

Single Domain Generalization Benchmark: Current State and Challenges

K. Imbulgoda Ruwan Tennakoon W. Chuah Steven Korevaar Feng Xia A. Bab-Hadiashar
Aug 2026 · International Journal of Computer Vision · Vol 134 · 0 citations · 113 references

TL;DR

Although SDG methods improve performance under highly domain distinguishable stylized shifts, they exhibit limited robustness to background, correlation and corruption shifts on larger datasets, and increased shape bias does not consistently yield enhanced OOD performance, thereby underscoring the need for further research into developing more resilient and generalizable models.

Abstract

Recent advances in deep learning have enabled remarkable success across computer vision tasks using massive datasets, yet the persistent challenge of domain shift-where training and testing distributions differ-continues to degrade model performance. While Domain Generalization techniques aim to learn invariant representations across multiple domains, their reliance on diverse training domains limits applicability in settings where multiple source domains are not available. This survey examines Single Domain Generalization (SDG) approaches that leverage targeted inductive biases to enhance out-of-domain (OOD) performance using a single source domain, addressing key questions regarding their effectiveness across various domain shifts, different model architectures such as CNNs and Vision transformers, and the impact of shape bias for OOD generalization. These questions are crucial as they directly inform the development of robust models that can reliably operate under real-world conditions, guiding the research community in overcoming limitations of current techniques. Our findings indicate that although SDG methods improve performance under highly domain distinguishable stylized shifts, they exhibit limited robustness to background, correlation and corruption shifts on larger datasets, and increased shape bias does not consistently yield enhanced OOD performance, thereby underscoring the need for further research into developing more resilient and generalizable models. For reproducibility, the implementation and code used in our benchmark are available on our official https://github.com/Kavindya98/SDG-Benchmark GitHub repository.

DOI Semantic Scholar
Read PDF

Similar papers

Preprint Aug 2026

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning

Deep neural networks excel in various tasks but struggle to generalize across evolving data distributions, leading to significant performance degradation under domain shifts. Domain incremental learning (DIL) addresses this challenge by enabling models to continuously adapt while retaining prior knowledge. Among existing DIL approaches, the parameter-isolation paradigm achieves state-of-the-art performance. However, these methods often adopt a one-size-fits-all approach to adapt to new domains, resulting in either insufficient learning capacity or redundant parameters. In this work, we propose BPG, a unified framework that addresses both challenges through two complementary components: BPG-Adapter, which dynamically determines each domain's adapter hidden dimension based on domain-specific feature separability, and BPG-Inference, a soft domain mixture strategy that integrates multiple domain-specific models at test time, mitigating domain ID misselection. Experimental results on DomainNet, CDDB, and CORe50 demonstrate that BPG consistently outperforms uniform adapter-based approaches and hard domain selection strategies, achieving state-of-the-art average accuracy while reducing forgetting to as low as 0.22% on DomainNet.

Qiang Wang, Songlin Dong, Shaokun Wang et al. · 0 citations
Open access 2023

Deep Learning Enhancements Using Pretraining and Fine-Tuning

Experimental findings show that pretrained models outperform those trained from scratch in terms of accuracy, convergence speed, and robustness, and a unified framework is proposed to integrate both processes in a deep learning pipeline.

Kwame Nkosi · 0 citations
Open access Aug 2026

Benchmarking Pre-Trained Feature Extractors: A Comparative Study Across Deep Learning Tasks

This study systematically compares seven pre-trained feature extractors across three architectural families, convolutional neural networks (CNNs), Vision Transformers (ViTs), and self-supervised models to provide practical guidance on model selection for downstream deep learning tasks.

Rafeek Sibrikhan, M. Mufassirin · 0 citations
Open access Jul 2026

Uncertainty Generation Meta-training for Cross-domain Few-shot Learning

An innovative meta-learning framework augmented by uncertainty generation is proposed that improves the current state-of-the-art by an average of 3.11%, with key innovations in using probabilistic feature statistics, gradient-based uncertainty modeling, and causal feature extraction that enhance cross-domain knowledge transfer more effectively than existing techniques.

Keyang Cheng, Yuze Sun, Yue Yu et al. · 0 citations
Open access Jul 2024

Efficient unsupervised domain adaptation via self-supervised vision transformer and synergistic cross-domain alignment

Efficient Unsupervised Domain Adaptation (EUDA) is proposed, a parameter-efficient framework that leverages a frozen DINOv2 backbone as a feature extractor and updates only a lightweight bottleneck and classification head to promote both discriminative learning and cross-domain alignment.

Ali Abedi, Q. M. J. Wu, Ning Zhang et al. · 9 citations