Generalized Domain-Adaptive Dictionaries
Citations Over TimeTop 1% of 2013 papers
Abstract
Data-driven dictionaries have produced state-of-the-art results in various classification tasks. However, when the target data has a different distribution than the source data, the learned sparse representation may not be optimal. In this paper, we investigate if it is possible to optimally represent both source and target by a common dictionary. Specifically, we describe a technique which jointly learns projections of data in the two domains, and a latent dictionary which can succinctly represent both the domains in the projected low-dimensional space. An efficient optimization technique is presented, which can be easily kernelized and extended to multiple domains. The algorithm is modified to learn a common discriminative dictionary, which can be further used for classification. The proposed approach does not require any explicit correspondence between the source and target domains, and shows good results even when there are only a few labels available in the target domain. Various recognition experiments show that the method performs on par or better than competitive state-of-the-art methods.
Related Papers
- Stable Discriminative Dictionary Learning Via Discriminative Deviation(2012)
- → Accurate And Fast Fine-Grained Image Classification via Discriminative Learning(2019)3 cited
- → Discriminative Regions: A Substrate for Analyzing Life-Logging Image Sequences(2014)2 cited
- → Diffusion-TTA: Test-time Adaptation of Discriminative Models via Generative Feedback(2023)