# Zero-Shot Domain Adaptation via Kernel Regression on the Grassmannian

arXiv: https://arxiv.org/abs/1507.07830 · HTML: https://mlsanity.com/paper/1507.07830/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jul 2015.

## Built on by (most cited first, 4 of 4)

- [Zero-Shot Day-Night Domain Adaptation with a Physics Prior](https://mlsanity.com/paper/2108.05137/) (2021)
- [Target inductive methods for zero-shot regression](https://mlsanity.com/paper/2402.01252/) (2024)
- [Predictive Optimization with Zero-Shot Domain Adaptation](https://mlsanity.com/paper/2101.06233/) (2021)
- [Direct side information learning for zero-shot regression](https://mlsanity.com/paper/2402.01264/) (2024)

## Abstract (arXiv)

Most visual recognition methods implicitly assume the data distribution remains unchanged from training to testing. However, in practice domain shift often exists, where real-world factors such as lighting and sensor type change between train and test, and classifiers do not generalise from source to target domains. It is impractical to train separate models for all possible situations because collecting and labelling the data is expensive. Domain adaptation algorithms aim to ameliorate domain shift, allowing a model trained on a source to perform well on a different target domain. However, even for the setting of unsupervised domain adaptation, where the target domain is unlabelled, collecting data for every possible target domain is still costly. In this paper, we propose a new domain adaptation method that has no need to access either data or labels of the target domain when it can be described by a parametrised vector and there exits several related source domains within the same parametric space. It greatly reduces the burden of data collection and annotation, and our experiments show some promising results.