{"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-learning-representations-with","title":"Disentangling Learning Representations with Density Estimation","arxiv_id":"2302.04362","date":"2023-02-08","proceeding":null,"authors":["Eric Yeats","Frank Liu","Hai Li"],"abstract":"Disentangled learning representations have promising utility in many applications, but they currently suffer from serious reliability issues. We present Gaussian Channel Autoencoder (GCAE), a method which achieves reliable disentanglement via flexible density estimation of the latent space. GCAE avoids the curse of dimensionality of density estimation by disentangling subsets of its latent space with the Dual Total Correlation (DTC) metric, thereby representing its high-dimensional latent joint distribution as a collection of many low-dimensional conditional distributions. In our experiments, GCAE achieves highly competitive and reliable disentanglement scores compared with state-of-the-art baselines.","url_abs":"https://arxiv.org/abs/2302.04362v1","url_pdf":"https://arxiv.org/pdf/2302.04362v1.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-learning-representations-with","repo_url":"https://github.com/ericyeats/gcae-disentanglement","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"density-estimation","task_name":"Density Estimation"},{"task_slug":"disentanglement","task_name":"Disentanglement"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2302.04362","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.04362"}},"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/ericyeats/gcae-disentanglement","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"unverified":5},"by_repo_kind":{"official":{"samples":5,"ran":0,"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":0,"samples":[{"code_sha256_prefix":"e1d504336b0f31fc","entry":"default_init","repo":"ericyeats/gcae-disentanglement","repo_kind":"official","path":"src/architectures.py","file_url":"https://github.com/ericyeats/gcae-disentanglement/blob/HEAD/src/architectures.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":"e1d504336b0f31fc"}},{"code_sha256_prefix":"e6eefaef20706684","entry":"gaussian_reconstruction_dist","repo":"ericyeats/gcae-disentanglement","repo_kind":"official","path":"src/disentanglement_utils.py","file_url":"https://github.com/ericyeats/gcae-disentanglement/blob/HEAD/src/disentanglement_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":"e6eefaef20706684"}},{"code_sha256_prefix":"31238bf1de0249d3","entry":"prod","repo":"ericyeats/gcae-disentanglement","repo_kind":"official","path":"src/disentanglement_utils.py","file_url":"https://github.com/ericyeats/gcae-disentanglement/blob/HEAD/src/disentanglement_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":"31238bf1de0249d3"}},{"code_sha256_prefix":"a679c290cf909537","entry":"relu_init","repo":"ericyeats/gcae-disentanglement","repo_kind":"official","path":"src/architectures.py","file_url":"https://github.com/ericyeats/gcae-disentanglement/blob/HEAD/src/architectures.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":"a679c290cf909537"}},{"code_sha256_prefix":"0bf34c0eed403d8f","entry":"selu_init","repo":"ericyeats/gcae-disentanglement","repo_kind":"official","path":"src/architectures.py","file_url":"https://github.com/ericyeats/gcae-disentanglement/blob/HEAD/src/architectures.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":"0bf34c0eed403d8f"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}