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Practical real world datasets with plentiful categories introduce new challenges for unsupervised domain adaptation like small inter-class discriminability, that existing approaches relying on domain invariance alone cannot handle sufficiently well. In this work we propose MemSAC, which exploits sample level similarity across source and target domains to achieve discriminative transfer, along with architectures that scale to a large number of categories. For this purpose, we first introduce a memory augmented approach to efficiently extract pairwise similarity relations between labeled source and unlabeled target domain instances, suited to handle an arbitrary number of classes. Next, we propose and theoretically justify a novel variant of the contrastive loss to promote local consistency among within-class cross domain samples while enforcing separation between classes, thus preserving discriminative transfer from source to target. We validate the advantages of MemSAC with significant improvements over previous state-of-the-art on multiple challenging transfer tasks designed for large-scale adaptation, such as DomainNet with 345 classes and fine-grained adaptation on Caltech-UCSD birds dataset with 200 classes. We also provide in-depth analysis and insights into the effectiveness of MemSAC. Code is available on the project webpage https://tarun005.github.io/MemSAC.

We propose an approach for unsupervised domain adaptation for the task of estimating someone's age from a given face image. In order to avoid the propagation of racial bias in most publicly available face images datasets into inefficacy of models trained on them, we perform domain adaptation to motivate the predictor to learn features that are invariant to ethnicity, enhancing the generalization performance across faces of people from different ethnic backgrounds. Exploiting the ordinality of age, we also impose ranking constraints on the prediction of the model and design our model such that it takes as input a pair of images, and outputs both the relative age difference and the rank of the first identity with respect to the other in terms of their ages. Furthermore, we implement Multi-Dimensional Scaling to retrieve absolute ages from the predicted age differences from as few as two labelled images from the domain to be adapted to. We experiment with a publicly available dataset with age labels, dividing it into subsets based on the ethnicity labels, and evaluating the performance of our approach on the data from an ethnicity different from the one that the model is trained on. Additionally we impose a constraint to preserve the sanity of the predictions with respect to relative and absolute ages, and another to ensure the smoothness of the predictions with respect to the input. We experiment extensively and compare various domain adaptation approaches for the task of regression.

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