• Skip to main content
  • Skip to primary sidebar
AAAI

AAAI

Association for the Advancement of Artificial Intelligence

    • AAAI

      AAAI

      Association for the Advancement of Artificial Intelligence

  • About AAAIAbout AAAI
    • AAAI Officers and Committees
    • AAAI Staff
    • Bylaws of AAAI
    • AAAI Awards
      • Fellows Program
      • Classic Paper Award
      • Dissertation Award
      • Distinguished Service Award
      • Allen Newell Award
      • Outstanding Paper Award
      • Award for Artificial Intelligence for the Benefit of Humanity
      • Feigenbaum Prize
      • Patrick Henry Winston Outstanding Educator Award
      • Engelmore Award
      • AAAI ISEF Awards
      • Senior Member Status
      • Conference Awards
    • AAAI Resources
    • AAAI Mailing Lists
    • Past AAAI Presidential Addresses
    • Presidential Panel on Long-Term AI Futures
    • Past AAAI Policy Reports
      • A Report to ARPA on Twenty-First Century Intelligent Systems
      • The Role of Intelligent Systems in the National Information Infrastructure
    • AAAI Logos
    • News
  • aaai-icon_ethics-diversity-line-yellowEthics & Diversity
  • Conference talk bubbleConferences & Symposia
    • AAAI Conference
    • AIES AAAI/ACM
    • AIIDE
    • IAAI
    • ICWSM
    • HCOMP
    • Spring Symposia
    • Summer Symposia
    • Fall Symposia
    • Code of Conduct for Conferences and Events
  • PublicationsPublications
    • AAAI Press
    • AI Magazine
    • Conference Proceedings
    • AAAI Publication Policies & Guidelines
    • Request to Reproduce Copyrighted Materials
  • aaai-icon_ai-magazine-line-yellowAI Magazine
    • Issues and Articles
    • Author Guidelines
    • Editorial Focus
  • MembershipMembership
    • Member Login
    • Developing Country List
    • AAAI Chapter Program

  • Career CenterCareer Center
  • aaai-icon_ai-topics-line-yellowAITopics
  • aaai-icon_contact-line-yellowContact

Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 35 / No. 10: AAAI-21 Technical Tracks 10

NASTransfer: Analyzing Architecture Transferability in Large Scale Neural Architecture Search

February 1, 2023

Download PDF

Authors

Rameswar Panda

IBM Research MIT-IBM Watson AI Lab


Michele Merler

IBM Research


Mayoore S Jaiswal

IBM Research


Hui Wu

IBM Research MIT-IBM Watson AI Lab


Kandan Ramakrishnan

IBM Research


Ulrich Finkler

IBM Research


Chun-Fu Richard Chen

IBM Research MIT-IBM Watson AI Lab


Minsik Cho

IBM Research


Rogerio Feris

IBM Research MIT-IBM Watson AI Lab


David Kung

IBM Research


Bishwaranjan Bhattacharjee

IBM Research


DOI:

10.1609/aaai.v35i10.17121


Abstract:

Neural Architecture Search (NAS) is an open and challenging problem in machine learning. While NAS offers great promise, the prohibitive computational demand of most of the existing NAS methods makes it difficult to directly search the architectures on large-scale tasks. The typical way of conducting large scale NAS is to search for an architectural building block on a small dataset (either using a proxy set from the large dataset or a completely different small scale dataset) and then transfer the block to a larger dataset. Despite a number of recent results that show the promise of transfer from proxy datasets, a comprehensive evaluation of different NAS methods studying the impact of different source datasets has not yet been addressed. In this work, we propose to analyze the architecture transferability of different NAS methods by performing a series of experiments on large scale benchmarks such as ImageNet1K and ImageNet22K. We find that: (i) The size and domain of the proxy set does not seem to influence architecture performance on the target dataset. On average, transfer performance of architectures searched using completely different small datasets (e.g., CIFAR10) perform similarly to the architectures searched directly on proxy target datasets. However, design of proxy sets has considerable impact on rankings of different NAS methods. (ii) While different NAS methods show similar performance on a source dataset (e.g., CIFAR10), they significantly differ on the transfer performance to a large dataset (e.g., ImageNet1K). (iii) Even on large datasets, random sampling baseline is very competitive, but the choice of the appropriate combination of proxy set and search strategy can provide significant improvement over it. We believe that our extensive empirical analysis will prove useful for future design of NAS algorithms.

Topics: AAAI

Primary Sidebar

HOW TO CITE:

Rameswar Panda||Michele Merler||Mayoore S Jaiswal||Hui Wu||Kandan Ramakrishnan||Ulrich Finkler||Chun-Fu Richard Chen||Minsik Cho||Rogerio Feris||David Kung||Bishwaranjan Bhattacharjee NASTransfer: Analyzing Architecture Transferability in Large Scale Neural Architecture Search Proceedings of the AAAI Conference on Artificial Intelligence (2021) 9294-9302.

Rameswar Panda||Michele Merler||Mayoore S Jaiswal||Hui Wu||Kandan Ramakrishnan||Ulrich Finkler||Chun-Fu Richard Chen||Minsik Cho||Rogerio Feris||David Kung||Bishwaranjan Bhattacharjee NASTransfer: Analyzing Architecture Transferability in Large Scale Neural Architecture Search AAAI 2021, 9294-9302.

Rameswar Panda||Michele Merler||Mayoore S Jaiswal||Hui Wu||Kandan Ramakrishnan||Ulrich Finkler||Chun-Fu Richard Chen||Minsik Cho||Rogerio Feris||David Kung||Bishwaranjan Bhattacharjee (2021). NASTransfer: Analyzing Architecture Transferability in Large Scale Neural Architecture Search. Proceedings of the AAAI Conference on Artificial Intelligence, 9294-9302.

Rameswar Panda||Michele Merler||Mayoore S Jaiswal||Hui Wu||Kandan Ramakrishnan||Ulrich Finkler||Chun-Fu Richard Chen||Minsik Cho||Rogerio Feris||David Kung||Bishwaranjan Bhattacharjee. NASTransfer: Analyzing Architecture Transferability in Large Scale Neural Architecture Search. Proceedings of the AAAI Conference on Artificial Intelligence 2021 p.9294-9302.

Rameswar Panda||Michele Merler||Mayoore S Jaiswal||Hui Wu||Kandan Ramakrishnan||Ulrich Finkler||Chun-Fu Richard Chen||Minsik Cho||Rogerio Feris||David Kung||Bishwaranjan Bhattacharjee. 2021. NASTransfer: Analyzing Architecture Transferability in Large Scale Neural Architecture Search. "Proceedings of the AAAI Conference on Artificial Intelligence". 9294-9302.

Rameswar Panda||Michele Merler||Mayoore S Jaiswal||Hui Wu||Kandan Ramakrishnan||Ulrich Finkler||Chun-Fu Richard Chen||Minsik Cho||Rogerio Feris||David Kung||Bishwaranjan Bhattacharjee. (2021) "NASTransfer: Analyzing Architecture Transferability in Large Scale Neural Architecture Search", Proceedings of the AAAI Conference on Artificial Intelligence, p.9294-9302

Rameswar Panda||Michele Merler||Mayoore S Jaiswal||Hui Wu||Kandan Ramakrishnan||Ulrich Finkler||Chun-Fu Richard Chen||Minsik Cho||Rogerio Feris||David Kung||Bishwaranjan Bhattacharjee, "NASTransfer: Analyzing Architecture Transferability in Large Scale Neural Architecture Search", AAAI, p.9294-9302, 2021.

Rameswar Panda||Michele Merler||Mayoore S Jaiswal||Hui Wu||Kandan Ramakrishnan||Ulrich Finkler||Chun-Fu Richard Chen||Minsik Cho||Rogerio Feris||David Kung||Bishwaranjan Bhattacharjee. "NASTransfer: Analyzing Architecture Transferability in Large Scale Neural Architecture Search". Proceedings of the AAAI Conference on Artificial Intelligence, 2021, p.9294-9302.

Rameswar Panda||Michele Merler||Mayoore S Jaiswal||Hui Wu||Kandan Ramakrishnan||Ulrich Finkler||Chun-Fu Richard Chen||Minsik Cho||Rogerio Feris||David Kung||Bishwaranjan Bhattacharjee. "NASTransfer: Analyzing Architecture Transferability in Large Scale Neural Architecture Search". Proceedings of the AAAI Conference on Artificial Intelligence, (2021): 9294-9302.

Rameswar Panda||Michele Merler||Mayoore S Jaiswal||Hui Wu||Kandan Ramakrishnan||Ulrich Finkler||Chun-Fu Richard Chen||Minsik Cho||Rogerio Feris||David Kung||Bishwaranjan Bhattacharjee. NASTransfer: Analyzing Architecture Transferability in Large Scale Neural Architecture Search. AAAI[Internet]. 2021[cited 2023]; 9294-9302.


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
Copyright 2022, Association for the Advancement of
Artificial Intelligence 1900 Embarcadero Road, Suite
101, Palo Alto, California 94303 All Rights Reserved

We use cookies on our website to give you the most relevant experience by remembering your preferences and repeat visits. By clicking “Accept All”, you consent to the use of ALL the cookies. However, you may visit "Cookie Settings" to provide a controlled consent.
Cookie SettingsAccept All
Manage consent

Privacy Overview

This website uses cookies to improve your experience while you navigate through the website. Out of these, the cookies that are categorized as necessary are stored on your browser as they are essential for the working of basic functionalities of the website. We also use third-party cookies that help us analyze and understand how you use this website. These cookies will be stored in your browser only with your consent. You also have the option to opt-out of these cookies. But opting out of some of these cookies may affect your browsing experience.
Necessary
Always Enabled
Necessary cookies are absolutely essential for the website to function properly. These cookies ensure basic functionalities and security features of the website, anonymously.
CookieDurationDescription
cookielawinfo-checkbox-analytics11 monthsThis cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Analytics".
cookielawinfo-checkbox-functional11 monthsThe cookie is set by GDPR cookie consent to record the user consent for the cookies in the category "Functional".
cookielawinfo-checkbox-necessary11 monthsThis cookie is set by GDPR Cookie Consent plugin. The cookies is used to store the user consent for the cookies in the category "Necessary".
cookielawinfo-checkbox-others11 monthsThis cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Other.
cookielawinfo-checkbox-performance11 monthsThis cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Performance".
viewed_cookie_policy11 monthsThe cookie is set by the GDPR Cookie Consent plugin and is used to store whether or not user has consented to the use of cookies. It does not store any personal data.
Functional
Functional cookies help to perform certain functionalities like sharing the content of the website on social media platforms, collect feedbacks, and other third-party features.
Performance
Performance cookies are used to understand and analyze the key performance indexes of the website which helps in delivering a better user experience for the visitors.
Analytics
Analytical cookies are used to understand how visitors interact with the website. These cookies help provide information on metrics the number of visitors, bounce rate, traffic source, etc.
Advertisement
Advertisement cookies are used to provide visitors with relevant ads and marketing campaigns. These cookies track visitors across websites and collect information to provide customized ads.
Others
Other uncategorized cookies are those that are being analyzed and have not been classified into a category as yet.
SAVE & ACCEPT