Unsupervised Domain Adaptation with a Relaxed Covariate Shift Assumption
نویسندگان
چکیده
Domain adaptation addresses learning tasks where training is performed on data from one domain whereas testing is performed on data belonging to a different but related domain. Assumptions about the relationship between the source and target domains should lead to tractable solutions on the one hand, and be realistic on the other hand. Here we propose a generative domain adaptation model that allows for modelling different assumptions about this relationship, among which is a newly introduced assumption that replaces covariate shift with a possibly more realistic assumption without losing tractability due to the efficient variational inference procedure developed. In addition to the ability to model less restrictive relationships between source and target, modelling can be performed without any target labeled data (unsupervised domain adaptation). We also provide a Rademacher complexity bound of the proposed algorithm. We evaluate the model on the Amazon reviews and the CVC pedestrian detection datasets.
منابع مشابه
A Probabilistic Covariate Shift Assumption for Domain Adaptation
The aim of domain adaptation algorithms is to establish a learner, trained on labeled data from a source domain, that can classify samples from a target domain, in which few or no labeled data are available for training. Covariate shift, a primary assumption in several works on domain adaptation, assumes that the labeling functions of source and target domains are identical. We present a domain...
متن کاملImage alignment via kernelized feature learning
Machine learning is an application of artificial intelligence that is able to automatically learn and improve from experience without being explicitly programmed. The primary assumption for most of the machine learning algorithms is that the training set (source domain) and the test set (target domain) follow from the same probability distribution. However, in most of the real-world application...
متن کاملNo Bias Left behind: Covariate Shift Adaptation for Discriminative 3D Pose Estimation
Discriminative, or (structured) prediction, methods have proved effective for variety of problems in computer vision; a notable example is 3D monocular pose estimation. All methods to date, however, relied on an assumption that training (source) and test (target) data come from the same underlying joint distribution. In many real cases, including standard datasets, this assumption is flawed. In...
متن کاملDeep Unsupervised Domain Adaptation for Image Classification via Low Rank Representation Learning
Domain adaptation is a powerful technique given a wide amount of labeled data from similar attributes in different domains. In real-world applications, there is a huge number of data but almost more of them are unlabeled. It is effective in image classification where it is expensive and time-consuming to obtain adequate label data. We propose a novel method named DALRRL, which consists of deep ...
متن کاملA Literature Review of Domain Adaptation with Unlabeled Data
In supervised learning, it is typically assumed that the labeled training data comes from the same distribution as the test data to which the system will be applied. In recent years, machine-learning researchers have investigated methods to handle mismatch between the training and test domains, with the goal of building a classifier using the labeled data in the old domain that will perform wel...
متن کامل