Existing domain adaptation methods assume that domain discrepancies are
caused by a few discrete attributes and variations, e.g., art, real, painting,
quickdraw, etc. We argue that this is not realistic as it is implausible to
define the real-world datasets using a few discrete attributes. Therefore, we
propose to investigate a new problem namely the Continuous Domain Adaptation
(CDA) through the lens where infinite domains are formed by continuously
varying attributes. Leveraging knowledge of two labeled source domains and
several observed unlabeled target domains data, the objective of CDA is to
learn a generalized model for whole data distribution with the continuous
attribute. Besides the contributions of formulating a new problem, we also
propose a novel approach as a strong CDA baseline. To be specific, firstly we
propose a novel alternating training strategy to reduce discrepancies among
multiple domains meanwhile generalize to unseen target domains. Secondly, we
propose a continuity constraint when estimating the cross-domain divergence
measurement. Finally, to decouple the discrepancy from the mini-batch size, we
design a domain-specific queue to maintain the global view of the source domain
that further boosts the adaptation performances. Our method is proven to
achieve the state-of-the-art in CDA problem using extensive experiments. The
code is available at this https URL

本文提出了连续域自适应（CDA）问题，并提出了一种新颖的方法作为 CDA 基线。该方法通过交替训练策略、连续性约束和域特定队列等手段，在多个域之间降低差异性并泛化到未见目标域，从而在 CDA 问题中实现了最优性能。