Multiple Subspace Alignment Improves Domain Adaptation
This addresses domain adaptation for cross-domain visual recognition, but it appears incremental as it builds on existing subspace methods.
The paper tackled the problem of limited performance in unsupervised domain adaptation for visual recognition by proposing a method that uses multiple subspaces instead of a single one, achieving state-of-the-art results on two benchmarks.
We present a novel unsupervised domain adaptation (DA) method for cross-domain visual recognition. Though subspace methods have found success in DA, their performance is often limited due to the assumption of approximating an entire dataset using a single low-dimensional subspace. Instead, we develop a method to effectively represent the source and target datasets via a collection of low-dimensional subspaces, and subsequently align them by exploiting the natural geometry of the space of subspaces, on the Grassmann manifold. We demonstrate the effectiveness of this approach, using empirical studies on two widely used benchmarks, with state of the art domain adaptation performance