{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/on-the-transfer-of-inductive-bias-from","title":"On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset","arxiv_id":"1906.03292","date":"2019-06-07","proceeding":"NeurIPS 2019 12","authors":["Muhammad Waleed Gondal","Manuel Wüthrich","Đorđe Miladinović","Francesco Locatello","Martin Breidt","Valentin Volchkov","Joel Akpo","Olivier Bachem","Bernhard Schölkopf","Stefan Bauer"],"abstract":"Learning meaningful and compact representations with 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 one million 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. We provide a first experimental study of these questions and our results indicate that learned models transfer poorly, but that model and hyperparameter selection is an effective means of transferring information to the real world.","url_abs":"https://arxiv.org/abs/1906.03292v3","url_pdf":"https://arxiv.org/pdf/1906.03292v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"on-the-transfer-of-inductive-bias-from","repo_url":"https://github.com/rr-learning/disentanglement_dataset","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"on-the-transfer-of-inductive-bias-from","repo_url":"https://github.com/andreinicolicioiu/dci-es","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"on-the-transfer-of-inductive-bias-from","repo_url":"https://github.com/causality-and-transfer-learning/disentanglement_dataset","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"CC-BY-4.0"}},{"paper_slug":"on-the-transfer-of-inductive-bias-from","repo_url":"https://github.com/facebookresearch/disentangling-correlated-factors","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"disentanglement","task_name":"Disentanglement"},{"task_slug":"inductive-bias","task_name":"Inductive Bias"},{"task_slug":"representation-learning","task_name":"Representation Learning"}],"methods":[],"datasets_introduced":[{"slug":"mpi3d-disentanglement","name":"MPI3D Disentanglement","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1906.03292","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1906.03292"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/facebookresearch/disentangling-correlated-factors","reach":{"status":"ok","spdx":"MIT"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/rr-learning/disentanglement_dataset","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/causality-and-transfer-learning/disentanglement_dataset","reach":{"status":"ok","spdx":"CC-BY-4.0"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/andreinicolicioiu/dci-es","reach":{"status":"ok","spdx":"Apache-2.0"}}],"summary":{"ran_draft_wrong":1},"by_repo_kind":{"official":{"samples":1,"ran":1,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":1,"samples":[{"code_sha256_prefix":"d39d1cd46009e948","entry":"numericalSort","repo":"rr-learning/disentanglement_dataset","repo_kind":"official","path":"read_data.py","file_url":"https://github.com/rr-learning/disentanglement_dataset/blob/HEAD/read_data.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"CC-BY-4.0","inline_ok":false,"mcp_get_code":{"code_sha256":"d39d1cd46009e948"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}