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Deep neural networks often require copious amount of labeled-data to traintheir scads of parameters. Training larger and deeper networks is hard withoutappropriate regularization, particularly while using a small dataset.Laterally, collecting well-annotated data is expensive, time-consuming andoften infeasible. A popular way to regularize these networks is to simply trainthe network with more data from an alternate representative dataset. This canlead to adverse effects if the statistics of the representative dataset aredissimilar to our target. This predicament is due to the problem of domainshift. Data from a shifted domain might not produce bespoke features when afeature extractor from the representative domain is used. In this paper, wepropose a new technique ($d$-SNE) of domain adaptation that cleverly usesstochastic neighborhood embedding techniques and a novel modified-Hausdorffdistance. The proposed technique is learnable end-to-end and is therefore,ideally suited to train neural networks. Extensive experiments demonstrate that$d$-SNE outperforms the current states-of-the-art and is robust to thevariances in different datasets, even in the one-shot and semi-supervisedlearning settings. $d$-SNE also demonstrates the ability to generalize tomultiple domains concurrently.

$d$-SNE: Domain Adaptation using Stochastic Neighborhood Embedding