Domain Adaptation Ben David

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.

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. Al 2006 let is a hypothesis class of vc dimension d. Shai ben david john blitzer koby. Of computer science university of toronto toronto on can tl cs toronto edu department of computing science university of alberta edmonton ab can.

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. Crammer and fernando c pereira booktitle nips year 2006. In this work we investigate the problem of domain adaptation.

Existing fsl model it explicitly addresses the domain shift problem caused by the difference between the seen and un seen classes in a two sub episode meta training framework. 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. However several disconnections still exist and form the gap between theory and algorithm. We bound the margin violation rate in the target domain by its counterpart from the source domain a novel.

Is the empirical source risk. H divergence와 vc dimension에 대한 가장 기본적인 개념을 이해하면 domain adaptation의 기본 정리를 볼 차례입니다. 2 the majority of recent da works 28 lay emphasis on how to minimize the domain divergence. Mansour et al 2009 ben david et al 2010.

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. In this paper we provide a novel theoretical study of the unsupervised domain adaptation problem that provides the following contributions to the field. Existing domain adaptation theories naturally imply minimax optimization algorithms which connect well with the domain adaptation methods based on adversarial learning. Analysis of representations for domain adaptation shai ben david school of computer science university of waterloo shai cs uwaterloo ca john blitzer koby crammer and fernando pereira department of computer and information science university of pennsylvania blitzer crammer pereira cis upenn edu abstract.

Domain adaptation based on the theory of ben david et al.