2021/03/01 by Prashant Pandey, Pandey, Prashant, Mrigank Raman +5 · 4 citations
Computer Science · Mathematics · #Artificial intelligence #Computer science #Domain (mathematical analysis) #Domain Adaptation and Few-Shot Learning #Generalization #Inference #Machine Learning and Data Classification #Mathematics #Multimodal Machine Learning Applications #Pattern recognition (psychology) #cs.CV #cs.LG
paper · pdf · doi:10.48550/arxiv.2103.01134
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2021/03/01 · openalex created_date 2021/03/15 · arxiv created 2021/07/19 · arxiv updated 2021/07/20 · openalex updated_date 2026/07/28
Generalization of machine learning models trained on a set of source domains on unseen target domains with different statistics, is a challenging problem. While many approaches have been proposed to solve this problem, they only utilize source data during training but do not take advantage of the fact that a single target example is available at the time of inference. Motivated by this, we propose a method that effectively uses the target sample during inference beyond mere classification. Our method has three components - (i) A label-preserving feature or metric transformation on source data such that the source samples are clustered in accordance with their class irrespective of their domain (ii) A generative model trained on the these features (iii) A label-preserving projection of the target point on the source-feature manifold during inference via solving an optimization problem on the input space of the generative model using the learned metric. Finally, the projected target is used in the classifier. Since the projected target feature comes from the source manifold and has the same label as the real target by design, the classifier is expected to perform better on it than the true target. We demonstrate that our method outperforms the state-of-the-art Domain Generalization methods on multiple datasets and tasks.