# Discriminative Joint Probability Maximum Mean Discrepancy (DJP-MMD) for Domain Adaptation

arXiv: https://arxiv.org/abs/1912.00320 · HTML: https://mlsanity.com/paper/1912.00320/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Dec 2019.

## Built on by (most cited first, 15 of 15)

- [Source-Free Unsupervised Domain Adaptation: A Survey](https://mlsanity.com/paper/2301.00265/) (2022)
- [EventDance: Unsupervised Source-free Cross-modal Adaptation for Event-based Object Recognition](https://mlsanity.com/paper/2403.14082/) (2024)
- [Certified Zeroth-order Black-Box Defense with Robust UNet Denoiser](https://mlsanity.com/paper/2304.06430/) (2023)
- [CATS: Mitigating Correlation Shift for Multivariate Time Series Classification](https://mlsanity.com/paper/2504.04283/) (2025)
- [The Conditional Cauchy-Schwarz Divergence with Applications to Time-Series Data and Sequential Decision Making](https://mlsanity.com/paper/2301.08970/) (2023)
- [Domain Adaptive Graph Neural Networks for Constraining Cosmological Parameters Across Multiple Data Sets](https://mlsanity.com/paper/2311.01588/) (2023)
- [Domain Adaptation with Cauchy-Schwarz Divergence](https://mlsanity.com/paper/2405.19978/) (2024)
- [Simulations of Common Unsupervised Domain Adaptation Algorithms for Image Classification](https://mlsanity.com/paper/2502.10694/) (2025)
- [Stylized Face Sketch Extraction via Generative Prior with Limited Data](https://mlsanity.com/paper/2403.11263/) (2024)
- [GLA-DA: Global-Local Alignment Domain Adaptation for Multivariate Time Series](https://mlsanity.com/paper/2410.06671/) (2024)
- [EventDance++: Language-guided Unsupervised Source-free Cross-modal Adaptation for Event-based Object Recognition](https://mlsanity.com/paper/2409.12778/) (2024)
- [Unsupervised Domain Adaptation Using Compact Internal Representations](https://mlsanity.com/paper/2401.07207/) (2024)
- [Theoretically Guaranteed Distribution Adaptable Learning](https://mlsanity.com/paper/2411.02921/) (2024)
- [Source-Free Domain Adaptation via Multi-view Contrastive Learning](https://mlsanity.com/paper/2507.03321/) (2025)
- [Brain foundation model-guided source-selective domain adaptation for cross-subject EEG decoding](https://mlsanity.com/paper/2507.21037/) (2025)

## Abstract (arXiv)

Maximum mean discrepancy (MMD) has been widely adopted in domain adaptation to measure the discrepancy between the source and target domain distributions. Many existing domain adaptation approaches are based on the joint MMD, which is computed as the (weighted) sum of the marginal distribution discrepancy and the conditional distribution discrepancy; however, a more natural metric may be their joint probability distribution discrepancy. Additionally, most metrics only aim to increase the transferability between domains, but ignores the discriminability between different classes, which may result in insufficient classification performance. To address these issues, discriminative joint probability MMD (DJP-MMD) is proposed in this paper to replace the frequently-used joint MMD in domain adaptation. It has two desirable properties: 1) it provides a new theoretical basis for computing the distribution discrepancy, which is simpler and more accurate; 2) it increases the transferability and discriminability simultaneously. We validate its performance by embedding it into a joint probability domain adaptation framework. Experiments on six image classification datasets demonstrated that the proposed DJP-MMD can outperform traditional MMDs.