{"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/disentangling-factors-of-variation-with-cycle","title":"Disentangling Factors of Variation with Cycle-Consistent Variational Auto-Encoders","arxiv_id":"1804.10469","date":"2018-04-27","proceeding":"ECCV 2018 9","authors":["Ananya Harsh Jha","Saket Anand","Maneesh Singh","V. S. R. Veeravasarapu"],"abstract":"Generative models that learn disentangled representations for different\nfactors of variation in an image can be very useful for targeted data\naugmentation. By sampling from the disentangled latent subspace of interest, we\ncan efficiently generate new data necessary for a particular task. Learning\ndisentangled representations is a challenging problem, especially when certain\nfactors of variation are difficult to label. In this paper, we introduce a\nnovel architecture that disentangles the latent space into two complementary\nsubspaces by using only weak supervision in form of pairwise similarity labels.\nInspired by the recent success of cycle-consistent adversarial architectures,\nwe use cycle-consistency in a variational auto-encoder framework. Our\nnon-adversarial approach is in contrast with the recent works that combine\nadversarial training with auto-encoders to disentangle representations. We show\ncompelling results of disentangled latent subspaces on three datasets and\ncompare with recent works that leverage adversarial training.","url_abs":"http://arxiv.org/abs/1804.10469v1","url_pdf":"http://arxiv.org/pdf/1804.10469v1.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":"disentangling-factors-of-variation-with-cycle","repo_url":"https://github.com/Akash-Sharma-1/Disentangling-Factors-of-Variation-in-Images-with-Cycle-Consistent-VAE","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"disentangling-factors-of-variation-with-cycle","repo_url":"https://github.com/ananyahjha93/challenges-in-disentangling","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"disentangling-factors-of-variation-with-cycle","repo_url":"https://github.com/ananyahjha93/cycle-consistent-vae","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"disentangling-factors-of-variation-with-cycle","repo_url":"https://github.com/ananyahjha93/disentangling-factors-of-variation-using-adversarial-training","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"disentangling-factors-of-variation-with-cycle","repo_url":"https://github.com/viditjain99/DL-Cycle-Consistent-Variational-Autoencoder","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"data-augmentation","task_name":"Data Augmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1804.10469","atlas_url":"https://app.syntology.ai/?focus=1804.10469","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1804.10469"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+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/ananyahjha93/disentangling-factors-of-variation-using-adversarial-training","reach":{"status":"ok","spdx":"MIT"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/Akash-Sharma-1/Disentangling-Factors-of-Variation-in-Images-with-Cycle-Consistent-VAE","reach":{"status":"ok","spdx":"MIT"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/ananyahjha93/cycle-consistent-vae","reach":{"status":"ok","spdx":"MIT"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/viditjain99/DL-Cycle-Consistent-Variational-Autoencoder","reach":{"status":"ok"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/ananyahjha93/challenges-in-disentangling","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"unverified":3},"by_repo_kind":{"listed":{"samples":3,"ran":0,"repositories":2}},"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":0,"samples":[{"code_sha256_prefix":"bfefebe8546a28f2","entry":"l1_loss","repo":"ananyahjha93/challenges-in-disentangling","repo_kind":"listed","path":"utils.py","file_url":"https://github.com/ananyahjha93/challenges-in-disentangling/blob/HEAD/utils.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"bfefebe8546a28f2"}},{"code_sha256_prefix":"59048fa620bc0bfb","entry":"mse_loss","repo":"ananyahjha93/challenges-in-disentangling","repo_kind":"listed","path":"utils.py","file_url":"https://github.com/ananyahjha93/challenges-in-disentangling/blob/HEAD/utils.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"59048fa620bc0bfb"}},{"code_sha256_prefix":"579f5a3676c471b2","entry":"reparameterize","repo":"ananyahjha93/cycle-consistent-vae","repo_kind":"listed","path":"utils.py","file_url":"https://github.com/ananyahjha93/cycle-consistent-vae/blob/HEAD/utils.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"579f5a3676c471b2"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}