# Grad-CL: Source Free Domain Adaptation with Gradient Guided Feature Disalignment

arXiv: https://arxiv.org/abs/2509.10134 · HTML: https://mlsanity.com/paper/2509.10134/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 33 references that are in the map.
- First posted on arXiv 12 Sep 2025.

## Builds on (most cited first, 25 of 25)

- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?](https://mlsanity.com/paper/1703.04977/) (2017)
- [Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2002.08546/) (2020)
- [ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation](https://mlsanity.com/paper/1811.12833/) (2018)
- [Model Adaptation: Historical Contrastive Learning for Unsupervised Domain Adaptation without Source Data](https://mlsanity.com/paper/2110.03374/) (2021)
- [A Comprehensive Survey on Source-free Domain Adaptation](https://mlsanity.com/paper/2302.11803/) (2023)
- [Deep Unsupervised Domain Adaptation: A Review of Recent Advances and Perspectives](https://mlsanity.com/paper/2208.07422/) (2022)
- [Open Compound Domain Adaptation](https://mlsanity.com/paper/1909.03403/) (2019)
- [Maximum Density Divergence for Domain Adaptation](https://mlsanity.com/paper/2004.12615/) (2020)
- [Source-Free Domain Adaptive Fundus Image Segmentation with Denoised Pseudo-Labeling](https://mlsanity.com/paper/2109.09735/) (2021)
- [Divide and Contrast: Source-free Domain Adaptation via Adaptive Contrastive Learning](https://mlsanity.com/paper/2211.06612/) (2022)
- [Source-Relaxed Domain Adaptation for Image Segmentation](https://mlsanity.com/paper/2005.03697/) (2020)
- [Boundary and Entropy-driven Adversarial Learning for Fundus Image Segmentation](https://mlsanity.com/paper/1906.11143/) (2019)
- [Patch-based Output Space Adversarial Learning for Joint Optic Disc and Cup Segmentation](https://mlsanity.com/paper/1902.07519/) (2019)
- [Local contrastive loss with pseudo-label based self-training for semi-supervised medical image segmentation](https://mlsanity.com/paper/2112.09645/) (2021)
- [Source-Free Domain Adaptive Fundus Image Segmentation with Class-Balanced Mean Teacher](https://mlsanity.com/paper/2307.09973/) (2023)
- [Unsupervised Robust Domain Adaptation without Source Data](https://mlsanity.com/paper/2103.14577/) (2021)
- [Nearest Neighborhood-Based Deep Clustering for Source Data-absent Unsupervised Domain Adaptation](https://mlsanity.com/paper/2107.12585/) (2021)
- [Target and Task specific Source-Free Domain Adaptive Image Segmentation](https://mlsanity.com/paper/2203.15792/) (2022)
- [Context-Aware Pseudo-Label Refinement for Source-Free Domain Adaptive Fundus Image Segmentation](https://mlsanity.com/paper/2308.07731/) (2023)
- [Unsupervised Domain Adaptive Fundus Image Segmentation with Category-level Regularization](https://mlsanity.com/paper/2207.03684/) (2022)
- [Uncertainty and Energy based Loss Guided Semi-Supervised Semantic Segmentation](https://mlsanity.com/paper/2501.01640/) (2025)
- [Label Calibration in Source Free Domain Adaptation](https://mlsanity.com/paper/2501.07072/) (2025)

## Built on by (most cited first, 1 of 1)

- [Prototype-Rectified Iterative Self-supervised Manifold Denoising under Severe Acoustic Shift](https://mlsanity.com/paper/2608.15037/) (2026)

## Abstract (arXiv)

Accurate segmentation of the optic disc and cup is critical for the early diagnosis and management of ocular diseases such as glaucoma. However, segmentation models trained on one dataset often suffer significant performance degradation when applied to target data acquired under different imaging protocols or conditions. To address this challenge, we propose Grad-CL, a novel source-free domain adaptation framework that leverages a pre-trained source model and unlabeled target data to robustly adapt segmentation performance without requiring access to the original source data. Grad-CL combines a gradient-guided pseudolabel refinement module with a cosine similarity-based contrastive learning strategy. In the first stage, salient class-specific features are extracted via a gradient-based mechanism, enabling more accurate uncertainty quantification and robust prototype estimation for refining noisy pseudolabels. In the second stage, a contrastive loss based on cosine similarity is employed to explicitly enforce inter-class separability between the gradient-informed features of the optic cup and disc. Extensive experiments on challenging cross-domain fundus imaging datasets demonstrate that Grad-CL outperforms state-of-the-art unsupervised and source-free domain adaptation methods, achieving superior segmentation accuracy and improved boundary delineation. Project and code are available at https://visdomlab.github.io/GCL/.