Towards Adapting ImageNet to Reality: Scalable Domain Adaptation with Implicit Low-rank Transformations
This addresses performance degradation in real-world applications of classifiers, though it appears incremental in adapting existing methods.
The paper tackles the problem of domain shift between internet image collections and real-world scenes by learning scalable domain adaptation classifiers with implicit low-rank transformations, achieving efficient learning and application to new categories.
Images seen during test time are often not from the same distribution as images used for learning. This problem, known as domain shift, occurs when training classifiers from object-centric internet image databases and trying to apply them directly to scene understanding tasks. The consequence is often severe performance degradation and is one of the major barriers for the application of classifiers in real-world systems. In this paper, we show how to learn transform-based domain adaptation classifiers in a scalable manner. The key idea is to exploit an implicit rank constraint, originated from a max-margin domain adaptation formulation, to make optimization tractable. Experiments show that the transformation between domains can be very efficiently learned from data and easily applied to new categories. This begins to bridge the gap between large-scale internet image collections and object images captured in everyday life environments.