Improving Non-Transferable Representation Learning by Harnessing Content and Style

Non-transferable learning (NTL) aims to restrict the generalization of models toward the target domain(s). To this end, existing works learn non-transferable representations by reducing statistical dependence between the source and target domain. However, such statistical methods essentially neglect to distinguish between styles and contents, leading them to inadvertently fit (i) spurious correlation between styles and labels, and (ii) fake independence between contents and labels. Consequently, their performance will be limited when natural distribution shifts occur or malicious intervention is imposed. In this paper, we propose a novel method (dubbed as H-NTL) to understand and advance the NTL problem by introducing a causal model to separately model content and style as two latent factors, based on which we disentangle and harness them as guidances for learning non-transferable representations with intrinsically causal relationships. Specifically, to avoid fitting spurious correlation and fake independence, we propose a variational inference framework to disentangle the naturally mixed content factors and style factors under our causal model. Subsequently, based on dual-path knowledge distillation, we harness the disentangled two factors as guidances for non-transferable representation learning: (i) we constraint the source domain representations to fit content factors (which are the intrinsic cause of labels), and (ii) we enforce that the target domain representations fit style factors which barely can predict labels. As a result, the learned feature representations follow optimal untransferability toward the target domain and minimal negative influence on the source domain, thus enabling better NTL performance. Empirically, the proposed H-NTL significantly outperforms competing methods by a large margin.