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We focus on the problem of domain adaptation when the goal is shifting themodel towards the target distribution, rather than learning domain invariantrepresentations. It has been shown that under the following two assumptions:(a) access to samples from intermediate distributions, and (b) samples beingannotated with the amount of change from the source distribution, self-trainingcan be successfully applied on gradually shifted samples to adapt the modeltoward the target distribution. We hypothesize having (a) is enough to enableiterative self-training to slowly adapt the model to the target distribution,by making use of an implicit curriculum. In the case where (a) does not hold,we observe that iterative self-training falls short. We propose GIFT, a methodthat creates virtual samples from intermediate distributions by interpolatingrepresentations of examples from source and target domains. We evaluate aniterative-self-training method on datasets with natural distribution shifts,and show that when applied on top of other domain adaptation methods, itimproves the performance of the model on the target dataset. We run an analysison a synthetic dataset to show that in the presence of (a)iterative-self-training naturally forms a curriculum of samples. Furthermore,we show that when (a) does not hold, GIFT performs better than iterativeself-training.

Gradual Domain Adaptation in the Wild:When Intermediate Distributions are Absent