Abstract

With the advancements in many data mining and machine learning tasks, together with the availability of large-scale annotated data sets, there have been an increasing number of off-the-shelf tools for addressing these tasks, like Stanford NLP Toolkit and Caffe Model Zoo. However, many of these tasks are time-evolving in nature due to, e.g., the emergence of new features and the change of class conditional distribution of features. As a result, the off-the-shelf tools are not able to adapt to such changes and will suffer from sub-optimal performance in the target application. In this paper, we propose a generic framework named AOT for adapting the outputs from an off-the-shelf tool to accommodate the changes in the learning task. It considers two major types of changes, i.e., label deficiency and distribution shift, and aims to maximally boost the performance of the off-the-shelf tool in the target domain, with the help of a limited number of target domain labeled examples. Furthermore, we propose an iterative algorithm to solve the resulting optimization problem, and we demonstrate the superior performance of the proposed AOT framework on text and image data sets.

Original languageEnglish (US)
Title of host publicationProceedings - 2018 IEEE International Conference on Big Data, Big Data 2018
EditorsYang Song, Bing Liu, Kisung Lee, Naoki Abe, Calton Pu, Mu Qiao, Nesreen Ahmed, Donald Kossmann, Jeffrey Saltz, Jiliang Tang, Jingrui He, Huan Liu, Xiaohua Hu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages140-145
Number of pages6
ISBN (Electronic)9781538650356
DOIs
StatePublished - Jan 22 2019
Event2018 IEEE International Conference on Big Data, Big Data 2018 - Seattle, United States
Duration: Dec 10 2018Dec 13 2018

Publication series

NameProceedings - 2018 IEEE International Conference on Big Data, Big Data 2018

Conference

Conference2018 IEEE International Conference on Big Data, Big Data 2018
CountryUnited States
CitySeattle
Period12/10/1812/13/18

Fingerprint

Classifiers
Data mining
Learning systems
Labels
Availability

Keywords

  • distribution shift
  • label deficiency
  • off-the-shelf classifiers

ASJC Scopus subject areas

  • Computer Science Applications
  • Information Systems

Cite this

Nelakurthi, A. R., Maciejewski, R., & He, J. (2019). Source Free Domain Adaptation Using an Off-the-Shelf Classifier. In Y. Song, B. Liu, K. Lee, N. Abe, C. Pu, M. Qiao, N. Ahmed, D. Kossmann, J. Saltz, J. Tang, J. He, H. Liu, ... X. Hu (Eds.), Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018 (pp. 140-145). [8622112] (Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData.2018.8622112

Source Free Domain Adaptation Using an Off-the-Shelf Classifier. / Nelakurthi, Arun Reddy; Maciejewski, Ross; He, Jingrui.

Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018. ed. / Yang Song; Bing Liu; Kisung Lee; Naoki Abe; Calton Pu; Mu Qiao; Nesreen Ahmed; Donald Kossmann; Jeffrey Saltz; Jiliang Tang; Jingrui He; Huan Liu; Xiaohua Hu. Institute of Electrical and Electronics Engineers Inc., 2019. p. 140-145 8622112 (Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Nelakurthi, AR, Maciejewski, R & He, J 2019, Source Free Domain Adaptation Using an Off-the-Shelf Classifier. in Y Song, B Liu, K Lee, N Abe, C Pu, M Qiao, N Ahmed, D Kossmann, J Saltz, J Tang, J He, H Liu & X Hu (eds), Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018., 8622112, Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018, Institute of Electrical and Electronics Engineers Inc., pp. 140-145, 2018 IEEE International Conference on Big Data, Big Data 2018, Seattle, United States, 12/10/18. https://doi.org/10.1109/BigData.2018.8622112
Nelakurthi AR, Maciejewski R, He J. Source Free Domain Adaptation Using an Off-the-Shelf Classifier. In Song Y, Liu B, Lee K, Abe N, Pu C, Qiao M, Ahmed N, Kossmann D, Saltz J, Tang J, He J, Liu H, Hu X, editors, Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018. Institute of Electrical and Electronics Engineers Inc. 2019. p. 140-145. 8622112. (Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018). https://doi.org/10.1109/BigData.2018.8622112
Nelakurthi, Arun Reddy ; Maciejewski, Ross ; He, Jingrui. / Source Free Domain Adaptation Using an Off-the-Shelf Classifier. Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018. editor / Yang Song ; Bing Liu ; Kisung Lee ; Naoki Abe ; Calton Pu ; Mu Qiao ; Nesreen Ahmed ; Donald Kossmann ; Jeffrey Saltz ; Jiliang Tang ; Jingrui He ; Huan Liu ; Xiaohua Hu. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 140-145 (Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018).
@inproceedings{441636df93144528a7f009c13e4a7499,
title = "Source Free Domain Adaptation Using an Off-the-Shelf Classifier",
abstract = "With the advancements in many data mining and machine learning tasks, together with the availability of large-scale annotated data sets, there have been an increasing number of off-the-shelf tools for addressing these tasks, like Stanford NLP Toolkit and Caffe Model Zoo. However, many of these tasks are time-evolving in nature due to, e.g., the emergence of new features and the change of class conditional distribution of features. As a result, the off-the-shelf tools are not able to adapt to such changes and will suffer from sub-optimal performance in the target application. In this paper, we propose a generic framework named AOT for adapting the outputs from an off-the-shelf tool to accommodate the changes in the learning task. It considers two major types of changes, i.e., label deficiency and distribution shift, and aims to maximally boost the performance of the off-the-shelf tool in the target domain, with the help of a limited number of target domain labeled examples. Furthermore, we propose an iterative algorithm to solve the resulting optimization problem, and we demonstrate the superior performance of the proposed AOT framework on text and image data sets.",
keywords = "distribution shift, label deficiency, off-the-shelf classifiers",
author = "Nelakurthi, {Arun Reddy} and Ross Maciejewski and Jingrui He",
year = "2019",
month = "1",
day = "22",
doi = "10.1109/BigData.2018.8622112",
language = "English (US)",
series = "Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "140--145",
editor = "Yang Song and Bing Liu and Kisung Lee and Naoki Abe and Calton Pu and Mu Qiao and Nesreen Ahmed and Donald Kossmann and Jeffrey Saltz and Jiliang Tang and Jingrui He and Huan Liu and Xiaohua Hu",
booktitle = "Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018",

}

TY - GEN

T1 - Source Free Domain Adaptation Using an Off-the-Shelf Classifier

AU - Nelakurthi, Arun Reddy

AU - Maciejewski, Ross

AU - He, Jingrui

PY - 2019/1/22

Y1 - 2019/1/22

N2 - With the advancements in many data mining and machine learning tasks, together with the availability of large-scale annotated data sets, there have been an increasing number of off-the-shelf tools for addressing these tasks, like Stanford NLP Toolkit and Caffe Model Zoo. However, many of these tasks are time-evolving in nature due to, e.g., the emergence of new features and the change of class conditional distribution of features. As a result, the off-the-shelf tools are not able to adapt to such changes and will suffer from sub-optimal performance in the target application. In this paper, we propose a generic framework named AOT for adapting the outputs from an off-the-shelf tool to accommodate the changes in the learning task. It considers two major types of changes, i.e., label deficiency and distribution shift, and aims to maximally boost the performance of the off-the-shelf tool in the target domain, with the help of a limited number of target domain labeled examples. Furthermore, we propose an iterative algorithm to solve the resulting optimization problem, and we demonstrate the superior performance of the proposed AOT framework on text and image data sets.

AB - With the advancements in many data mining and machine learning tasks, together with the availability of large-scale annotated data sets, there have been an increasing number of off-the-shelf tools for addressing these tasks, like Stanford NLP Toolkit and Caffe Model Zoo. However, many of these tasks are time-evolving in nature due to, e.g., the emergence of new features and the change of class conditional distribution of features. As a result, the off-the-shelf tools are not able to adapt to such changes and will suffer from sub-optimal performance in the target application. In this paper, we propose a generic framework named AOT for adapting the outputs from an off-the-shelf tool to accommodate the changes in the learning task. It considers two major types of changes, i.e., label deficiency and distribution shift, and aims to maximally boost the performance of the off-the-shelf tool in the target domain, with the help of a limited number of target domain labeled examples. Furthermore, we propose an iterative algorithm to solve the resulting optimization problem, and we demonstrate the superior performance of the proposed AOT framework on text and image data sets.

KW - distribution shift

KW - label deficiency

KW - off-the-shelf classifiers

UR - http://www.scopus.com/inward/record.url?scp=85062612656&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85062612656&partnerID=8YFLogxK

U2 - 10.1109/BigData.2018.8622112

DO - 10.1109/BigData.2018.8622112

M3 - Conference contribution

T3 - Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018

SP - 140

EP - 145

BT - Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018

A2 - Song, Yang

A2 - Liu, Bing

A2 - Lee, Kisung

A2 - Abe, Naoki

A2 - Pu, Calton

A2 - Qiao, Mu

A2 - Ahmed, Nesreen

A2 - Kossmann, Donald

A2 - Saltz, Jeffrey

A2 - Tang, Jiliang

A2 - He, Jingrui

A2 - Liu, Huan

A2 - Hu, Xiaohua

PB - Institute of Electrical and Electronics Engineers Inc.

ER -