Physics

Random variable

Pattern recognition (psychology)

Marginal distribution

Metric (unit)

Domain adaptation

Mathematics

Domain (mathematical analysis)

Discriminative model

Algorithm

Optics

Statistics

Machine learning

Probability distribution

Engineering

Mathematical analysis

Adaptation (eye)

Joint (building)

Joint probability distribution

Artificial intelligence

Architectural engineering

Operations management

Computer science

Conditional probability distribution

Embedding

Classifier (UML)

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.

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