DeepAI AI Chat
Log In Sign Up

Discriminative and Geometry Aware Unsupervised Domain Adaptation

by   Lingkun Luo, et al.

Domain adaptation (DA) aims to generalize a learning model across training and testing data despite the mismatch of their data distributions. In light of a theoretical estimation of upper error bound, we argue in this paper that an effective DA method should 1) search a shared feature subspace where source and target data are not only aligned in terms of distributions as most state of the art DA methods do, but also discriminative in that instances of different classes are well separated; 2) account for the geometric structure of the underlying data manifold when inferring data labels on the target domain. In comparison with a baseline DA method which only cares about data distribution alignment between source and target, we derive three different DA models, namely CDDA, GA-DA, and DGA-DA, to highlight the contribution of Close yet Discriminative DA(CDDA) based on 1), Geometry Aware DA (GA-DA) based on 2), and finally Discriminative and Geometry Aware DA (DGA-DA) implementing jointly 1) and 2). Using both synthetic and real data, we show the effectiveness of the proposed approach which consistently outperforms state of the art DA methods over 36 image classification DA tasks through 6 popular benchmarks. We further carry out in-depth analysis of the proposed DA method in quantifying the contribution of each term of our DA model and provide insights into the proposed DA methods in visualizing both real and synthetic data.


page 9

page 17


Discriminative Label Consistent Domain Adaptation

Domain adaptation (DA) is transfer learning which aims to learn an effec...

Robust Data Geometric Structure Aligned Close yet Discriminative Domain Adaptation

Domain adaptation (DA) is transfer learning which aims to leverage label...

Discriminative Noise Robust Sparse Orthogonal Label Regression-based Domain Adaptation

Domain adaptation (DA) aims to enable a learning model trained from a so...

Task-Discriminative Domain Alignment for Unsupervised Domain Adaptation

Domain Adaptation (DA), the process of effectively adapting task models ...

Domain adaptation under structural causal models

Domain adaptation (DA) arises as an important problem in statistical mac...

Close Yet Distinctive Domain Adaptation

Domain adaptation is transfer learning which aims to generalize a learni...

Multi-component Image Translation for Deep Domain Generalization

Domain adaption (DA) and domain generalization (DG) are two closely rela...