Efficient Learning of Domain-invariant Image Representations

TL;DR

This paper introduces an efficient algorithm for learning domain-invariant image representations by jointly optimizing a linear transformation and classifier, improving accuracy and scalability across diverse datasets.

Contribution

The paper proposes a novel joint optimization method for a linear transformation and classifier that effectively handles domain mismatch in image recognition tasks.

Findings

Improved accuracy over previous methods

Enhanced computational efficiency

Effective across multiple image datasets

Abstract

We present an algorithm that learns representations which explicitly compensate for domain mismatch and which can be efficiently realized as linear classifiers. Specifically, we form a linear transformation that maps features from the target (test) domain to the source (training) domain as part of training the classifier. We optimize both the transformation and classifier parameters jointly, and introduce an efficient cost function based on misclassification loss. Our method combines several features previously unavailable in a single algorithm: multi-class adaptation through representation learning, ability to map across heterogeneous feature spaces, and scalability to large datasets. We present experiments on several image datasets that demonstrate improved accuracy and computational advantages compared to previous approaches.