Introduction to domain adaptation

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Presentation transcript:

Introduction to domain adaptation

The Need for Domain Adaptation source domain target domain

The Need for Domain Adaptation source domain target domain

The Need for Domain Adaptation source domain target domain

The Need for Domain Adaptation source domain target domain

Where Does the Difference Come from? p(x, y) ps(y | x) ≠ pt(y | x) p(x)p(y | x) ps(x) ≠ pt(x) labeling difference instance difference ? labeling adaptation instance adaptation

Methods for domain adaptation Sample importance weighting Change of Representation Semi-Supervised Learning Multi-Task Learning

Transductive Transfer Learning Instance-transfer Approaches Sample Selection Bias / Covariance Shift [Zadrozny ICML-04, Schwaighofer JSPI-00] Input: A lot of labeled data in the source domain and no labeled data in the target domain. Output: Models for use in the target domain data. Assumption: The source domain and target domain are the same. In addition, and are the same while and may be different causing by different sampling process (training data and test data). Main Idea: Re-weighting (important sampling) the source domain data.

Sample Selection Bias/Covariance Shift To correct sample selection bias: How to estimate ? One straightforward solution is to estimate and , respectively. However, estimating density function is a hard problem. weights for source domain data

To match means between training and test data in a RKHS Sample Selection Bias/Covariance Shift Kernel Mean Match (KMM) [Huang et al. NIPS 2006] Main Idea: KMM tries to estimate directly instead of estimating density function. It can be proved that can be estimated by solving the following quadratic programming (QP) optimization problem. Theoretical Support: Maximum Mean Discrepancy (MMD) [Borgwardt et al. BIOINFOMATICS-06]. The distance of distributions can be measured by Euclid distance of their mean vectors in a RKHS. To match means between training and test data in a RKHS

Transductive Transfer Learning Feature-representation-transfer Approaches Domain Adaptation [Blitzer et al. EMNL-06, Ben-David et al. NIPS-07, Daume III ACL-07] Assumption: Single task across domains, which means and are the same while and may be different causing by feature representations across domains. Main Idea: Find a “good” feature representation that reduce the “distance” between domains. Input: A lot of labeled data in the source domain and only unlabeled data in the target domain. Output: A common representation between source domain data and target domain data and a model on the new representation for use in the target domain.

Extracting the shared feature subspace