TLR: Transfer Latent Representation for Unsupervised Domain Adaptation
This addresses domain adaptation for machine learning applications where labeled data is scarce in target domains, but it appears incremental as it builds on existing latent space approaches.
The paper tackles the problem of domain adaptation by proposing a novel method, Transfer Latent Representation (TLR), which learns a robust latent space using a linear autoencoder to preserve common properties and reduce noise, resulting in demonstrated advantages over competing methods in cross-domain tasks.
Domain adaptation refers to the process of learning prediction models in a target domain by making use of data from a source domain. Many classic methods solve the domain adaptation problem by establishing a common latent space, which may cause the loss of many important properties across both domains. In this manuscript, we develop a novel method, transfer latent representation (TLR), to learn a better latent space. Specifically, we design an objective function based on a simple linear autoencoder to derive the latent representations of both domains. The encoder in the autoencoder aims to project the data of both domains into a robust latent space. Besides, the decoder imposes an additional constraint to reconstruct the original data, which can preserve the common properties of both domains and reduce the noise that causes domain shift. Experiments on cross-domain tasks demonstrate the advantages of TLR over competing methods.