English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse
  1. Item Summary / 
  2. View item

Item

ITEM ACTIONSEXPORT
Add to Basket
 Local TagsRelease HistoryDetailsSummary
  On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset

Gondal, M., Wüthrich, M., Miladinovic, D., Locatello, F., Breidt, M., Volchkov, V., et al. (2020). On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset. In H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, & R. Garnett (Eds.), Advances in Neural Information Processing Systems 32 (NeurIPS 2019) (pp. 15661-15672). Red Hook, NY, USA: Curran.

Item is

 

show all hide all

Basic

show hide
Item Permalink: https://hdl.handle.net/21.11116/0000-0003-CAB1-1 Version Permalink: https://hdl.handle.net/21.11116/0000-000A-11A9-4
Genre: Conference Paper

Files

show Files

Locators

show
hide
Locator:
https://papers.nips.cc/paper/2019/file/d97d404b6119214e4a7018391195240a-Paper.pdf (Publisher version)
Description:
-
OA-Status:

Creators

show
hide
 Creators:
Gondal, MW, Author
Wüthrich, M, Author
Miladinovic, D, Author
Locatello, F, Author
Breidt, M1, 2, Author           
Volchkov, V, Author
Akpo, J, Author
Bachem, O, Author
Schölkopf, B3, Author           
Bauer, S, Author
Affiliations:
1Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497797              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              
3Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society, ou_1497647              

Content

show
hide
Free keywords: -
 Abstract: Learning meaningful and compact representations with structurally disentangled semantic aspects is considered to be of key importance in representation learning. Since real-world data is notoriously costly to collect, many recent state-of-the-art disentanglement models have heavily relied on synthetic toy data-sets. In this paper, we propose a novel data-set which consists of over 450'000 images of physical 3D objects with seven factors of variation, such as object color, shape, size and position. In order to be able to control all the factors of variation precisely, we built an experimental platform where the objects are being moved by a robotic arm. In addition, we provide two more datasets which consist of simulations of the experimental setup. These datasets provide for the first time the possibility to systematically investigate how well different disentanglement methods perform on real data in comparison to simulation, and how simulated data can be leveraged to build better representations of the real world.

Details

show
hide
Language(s):
 Dates: 2019-122020-06
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: -
 Degree: -

Event

show
hide
Title: 33rd Conference on Neural Information Processing Systems (NeurIPS 2019)
Place of Event: Vancouver, Canada
Start-/End Date: 2019-12-09 - 2019-12-13

Legal Case

show

Project information

show

Source 1

show
hide
Title: Advances in Neural Information Processing Systems 32 (NeurIPS 2019)
Source Genre: Proceedings
 Creator(s):
Wallach, H, Editor
Larochelle, H, Editor
Beygelzimer, A, Editor
d'Alché-Buc, F, Editor
Fox, E, Editor
Garnett, R, Editor
Affiliations:
-
Publ. Info: Red Hook, NY, USA : Curran
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 15661 - 15672 Identifier: ISBN: 978-1-7138-0793-3