Semi-supervised domain adaptation via subspace exploration

Recent methods of learning latent representations in Domain Adaptation (DA) often entangle the learning of features and exploration of latent space into a unified process. However, these methods can cause a false alignment problem and do not generalise well to the alignment of distributions with large discrepancy. In this study, the authors propose to explore a robust subspace for Semi-Supervised Domain Adaptation (SSDA) explicitly. To be concrete, for disentangling the intricate relationship between feature learning and subspace exploration, the authors iterate and optimise them in two steps: in the first step, the authors aim to learn well-clustered latent representations by aggregating the target feature around the estimated class-wise prototypes; in the second step, the authors adaptively explore a subspace of an autoencoder for robust SSDA. Specially, a novel denoising strategy via class-agnostic disturbance to improve the discriminative ability of subspace is adopted. Extensive experiments on publicly available datasets verify the promising and competitive performance of our approach against state-of-the-art methods.