# Discriminative Label Consistent Domain Adaptation

## TL;DR

This paper introduces a novel unsupervised domain adaptation method that enhances cross-domain visual recognition by optimizing data distribution divergence, inter-class distances, and label regression within a shared feature subspace, improving performance on standard benchmarks.

## Contribution

The proposed method uniquely combines distribution alignment, discriminative inter-class separation, and sparse label regression with outlier handling in an unsupervised setting.

## Key findings

- Outperforms state-of-the-art DA methods on 12 image classification tasks.
- Effectively reduces domain divergence and increases class separability.
- Handles data outliers to prevent negative transfer.

## Abstract

Domain adaptation (DA) is transfer learning which aims to learn an effective predictor on target data from source data despite data distribution mismatch between source and target. We present in this paper a novel unsupervised DA method for cross-domain visual recognition which simultaneously optimizes the three terms of a theoretically established error bound. Specifically, the proposed DA method iteratively searches a latent shared feature subspace where not only the divergence of data distributions between the source domain and the target domain is decreased as most state-of-the-art DA methods do, but also the inter-class distances are increased to facilitate discriminative learning. Moreover, the proposed DA method sparsely regresses class labels from the features achieved in the shared subspace while minimizing the prediction errors on the source data and ensuring label consistency between source and target. Data outliers are also accounted for to further avoid negative knowledge transfer. Comprehensive experiments and in-depth analysis verify the effectiveness of the proposed DA method which consistently outperforms the state-of-the-art DA methods on standard DA benchmarks, i.e., 12 cross-domain image classification tasks.