  The effectiveness of unsupervised domain adaptation degrades when there is a
large discrepancy between the source and target domains. Gradual domain
adaptation (GDA) is one promising way to mitigate such an issue, by leveraging
additional unlabeled data that gradually shift from the source to the target.
Through sequentially adapting the model along the "indexed" intermediate
domains, GDA substantially improves the overall adaptation performance. In
practice, however, the extra unlabeled data may not be separated into
intermediate domains and indexed properly, limiting the applicability of GDA.
In this paper, we investigate how to discover the sequence of intermediate
domains when it is not already available. Concretely, we propose a
coarse-to-fine framework, which starts with a coarse domain discovery step via
progressive domain discriminator training. This coarse domain sequence then
undergoes a fine indexing step via a novel cycle-consistency loss, which
encourages the next intermediate domain to preserve sufficient discriminative
knowledge of the current intermediate domain. The resulting domain sequence can
then be used by a GDA algorithm. On benchmark data sets of GDA, we show that
our approach, which we name Intermediate DOmain Labeler (IDOL), can lead to
comparable or even better adaptation performance compared to the pre-defined
domain sequence, making GDA more applicable and robust to the quality of domain
sequences. Codes are available at https://github.com/hongyouc/IDOL.
