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Dataset bias remains a significant barrier towards solving real worldcomputer vision tasks. Though deep convolutional networks have proven to be acompetitive approach for image classification, a question remains: have thesemodels have solved the dataset bias problem? In general, training orfine-tuning a state-of-the-art deep model on a new domain requires asignificant amount of data, which for many applications is simply notavailable. Transfer of models directly to new domains without adaptation hashistorically led to poor recognition performance. In this paper, we pose thefollowing question: is a single image dataset, much larger than previouslyexplored for adaptation, comprehensive enough to learn general deep models thatmay be effectively applied to new image domains? In other words, are deep CNNstrained on large amounts of labeled data as susceptible to dataset bias asprevious methods have been shown to be? We show that a generic supervised deepCNN model trained on a large dataset reduces, but does not remove, datasetbias. Furthermore, we propose several methods for adaptation with deep modelsthat are able to operate with little (one example per category) or no labeleddomain specific data. Our experiments show that adaptation of deep models onbenchmark visual domain adaptation datasets can provide a significantperformance boost.

One-Shot Adaptation of Supervised Deep Convolutional Models