Domain Transfer Structured Output Learning

TL;DR

This paper introduces the problem of domain transfer structured output learning, proposing a novel method to adapt predictors from a source domain to a target domain with limited labeled data, especially for structured outputs.

Contribution

It presents the first solution to domain transfer structured output learning by adapting an auxiliary predictor with a delta function for the target domain.

Findings

Effective adaptation of source predictors to target domain

Algorithm successfully minimizes loss on limited target data

First formalization and solution for domain transfer in structured output learning

Abstract

In this paper, we propose the problem of domain transfer structured output learn- ing and the first solution to solve it. The problem is defined on two different data domains sharing the same input and output spaces, named as source domain and target domain. The outputs are structured, and for the data samples of the source domain, the corresponding outputs are available, while for most data samples of the target domain, the corresponding outputs are missing. The input distributions of the two domains are significantly different. The problem is to learn a predictor for the target domain to predict the structured outputs from the input. Due to the limited number of outputs available for the samples form the target domain, it is difficult to directly learn the predictor from the target domain, thus it is necessary to use the output information available in source domain. We propose to learn the target domain predictor by adapting a auxiliary predictor trained by using source domain data to the target domain. The adaptation is implemented by adding a delta function on the basis of the auxiliary predictor. An algorithm is developed to learn the parameter of the delta function to minimize loss functions associat- ed with the predicted outputs against the true outputs of the data samples with available outputs of the target domain.