Domain Adaptation Ben David

In this paper we provide a novel theoretical study of the unsupervised domain adaptation problem that provides the following contributions to the field.

Domain adaptation ben david. We analyze a setting in which we have plentiful labeled training data drawn from one or more source distrib utions but little or no labeled training data drawn from the target distribution of interest. We bound the margin violation rate in the target domain by its counterpart from the source domain a novel. Analysis of representations for domain adaptation inproceedings bendavid2006analysisor title analysis of representations for domain adaptation author shai ben david and john blitzer and k.

We extend previ ous theories mansour et al 2009c ben david. Domain adaptation is a field associated with machine learning and transfer learning this scenario arises when we aim at learning from a source data distribution a well performing model on a different but related target data distribution. Domain adaptation pan yang 2010 aims to generalize the learned model to different domains or different distribu tions.

Domain adaptation based on the theory of ben david et al. Existing domain adaptation theories naturally imply minimax optimization algorithms which connect well with the domain adaptation methods based on adversarial learning. Crammer and fernando c pereira booktitle nips year 2006.

H divergence와 vc dimension에 대한 가장 기본적인 개념을 이해하면 domain adaptation의 기본 정리를 볼 차례입니다. However several disconnections still exist and form the gap between theory and algorithm. Shai ben david john blitzer koby.

Some methods 17 26 22 aim to align the latent feature distribution between two domains among which the most common strategy is to match the. Al 2006 let is a hypothesis class of vc dimension d. 2 the majority of recent da works 28 lay emphasis on how to minimize the domain divergence.

In this work we investigate the problem of domain adaptation. Built upon the theoretical contribution in ben david et al 2007b. Impossibility theorems for domain adaptation shai ben david and teresa luu tyler lu d avid p al school of computer science university of waterloo waterloo on can shai t2luu cs uwaterloo ca dept.