# Joint Geometrical and Statistical Alignment for Visual Domain Adaptation

arXiv: https://arxiv.org/abs/1705.05498 · HTML: https://mlsanity.com/paper/1705.05498/

## Key facts

- Cited by 43 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 May 2017.

## Built on by (most cited first, 30 of 43)

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- [Learning Compatible Embeddings](https://mlsanity.com/paper/2108.01958/) (2021)
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- [A Survey of Unsupervised Domain Adaptation for Visual Recognition](https://mlsanity.com/paper/2112.06745/) (2021)
- [Aligning Correlation Information for Domain Adaptation in Action Recognition](https://mlsanity.com/paper/2107.04932/) (2021)
- [Partial Video Domain Adaptation with Partial Adversarial Temporal Attentive Network](https://mlsanity.com/paper/2107.04941/) (2021)
- [Partial Identifiability for Domain Adaptation](https://mlsanity.com/paper/2306.06510/) (2023)
- [Cross-domain Speech Recognition with Unsupervised Character-level Distribution Matching](https://mlsanity.com/paper/2104.07491/) (2021)
- [Robust Local Preserving and Global Aligning Network for Adversarial Domain Adaptation](https://mlsanity.com/paper/2203.04156/) (2022)
- [Consistency and Diversity induced Human Motion Segmentation](https://mlsanity.com/paper/2202.04861/) (2022)
- [Improving Pseudo Labels With Intra-Class Similarity for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2207.12139/) (2022)
- [Effective Pseudo-Labeling based on Heatmap for Unsupervised Domain Adaptation in Cell Detection](https://mlsanity.com/paper/2303.05269/) (2023)
- [Attention Regularized Laplace Graph for Domain Adaptation](https://mlsanity.com/paper/2210.08170/) (2022)
- [Discriminative Noise Robust Sparse Orthogonal Label Regression-based Domain Adaptation](https://mlsanity.com/paper/2101.04563/) (2021)
- [On Causality in Domain Adaptation and Semi-Supervised Learning: an Information-Theoretic Analysis for Parametric Models](https://mlsanity.com/paper/2205.04641/) (2022)
- [3D Shape Knowledge Graph for Cross-domain 3D Shape Retrieval](https://mlsanity.com/paper/2210.15136/) (2022)
- [Unsupervised Domain Adaptation for Image Classification via Structure-Conditioned Adversarial Learning](https://mlsanity.com/paper/2103.02808/) (2021)
- [A Bayesian Approach to (Online) Transfer Learning: Theory and Algorithms](https://mlsanity.com/paper/2109.01377/) (2021)
- [Deep Least Squares Alignment for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2111.02207/) (2021)
- [A Multi-stage Framework with Mean Subspace Computation and Recursive Feedback for Online Unsupervised Domain Adaptation](https://mlsanity.com/paper/2207.00003/) (2022)
- [M3D: Manifold-based Domain Adaptation with Dynamic Distribution for Non-Deep Transfer Learning in Cross-subject and Cross-session EEG-based Emotion Recognition](https://mlsanity.com/paper/2404.15615/) (2024)
- [Enhancing ensemble learning and transfer learning in multimodal data analysis by adaptive dimensionality reduction](https://mlsanity.com/paper/2105.03682/) (2021)
- [Distributionally Robust Domain Adaptation](https://mlsanity.com/paper/2210.16894/) (2022)
- [Heterogeneous-Modal Unsupervised Domain Adaptation via Latent Space Bridging](https://mlsanity.com/paper/2506.15971/) (2025)
- [OSCAR: Open-Set CAD Retrieval from a Language Prompt and a Single Image](https://mlsanity.com/paper/2601.07333/) (2026)
- [A Unified Joint Maximum Mean Discrepancy for Domain Adaptation](https://mlsanity.com/paper/2101.09979/) (2021)
- [Efficient Transfer Learning via Joint Adaptation of Network Architecture and Weight](https://mlsanity.com/paper/2105.08994/) (2021)
- [Cross-domain error minimization for unsupervised domain adaptation](https://mlsanity.com/paper/2106.15057/) (2021)
- [Cycle Label-Consistent Networks for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2205.13957/) (2022)
- [Consecutive Knowledge Meta-Adaptation Learning for Unsupervised Medical Diagnosis](https://mlsanity.com/paper/2209.10425/) (2022)

## Abstract (arXiv)

This paper presents a novel unsupervised domain adaptation method for cross-domain visual recognition. We propose a unified framework that reduces the shift between domains both statistically and geometrically, referred to as Joint Geometrical and Statistical Alignment (JGSA). Specifically, we learn two coupled projections that project the source domain and target domain data into low dimensional subspaces where the geometrical shift and distribution shift are reduced simultaneously. The objective function can be solved efficiently in a closed form. Extensive experiments have verified that the proposed method significantly outperforms several state-of-the-art domain adaptation methods on a synthetic dataset and three different real world cross-domain visual recognition tasks.