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Vittorio Murino, Pietro Morerio
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There is an issue of domain shift in machine learning models, which occurs when models trained on one dataset perform poorly when tested on data from a different source. This problem is particularly relevant for visual models operating in the real world or when no labelled data is available for the target scenario. Domain adaptation (DA) algorithms aim to bridge the gap between these different input distributions. They work by using labelled data from a source domain to create a model for a target domain with limited or no labelled data. The text explains that various DA techniques have been developed to address this problem, with a focus on recent trends in deep neural networks. Additionally, it mentions domain generalisation, which is an even more challenging task because it assumes that target data is unavailable, requiring models to generalise effectively without specific adaptation to classify previously unseen samples.
