{"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/disentanglement-via-latent-quantization-1","title":"Disentanglement via Latent Quantization","arxiv_id":"2305.18378","date":"2023-05-28","proceeding":"NeurIPS 2023 11","authors":["Kyle Hsu","Will Dorrell","James C. R. Whittington","Jiajun Wu","Chelsea Finn"],"abstract":"In disentangled representation learning, a model is asked to tease apart a dataset's underlying sources of variation and represent them independently of one another. Since the model is provided with no ground truth information about these sources, inductive biases take a paramount role in enabling disentanglement. In this work, we construct an inductive bias towards encoding to and decoding from an organized latent space. Concretely, we do this by (i) quantizing the latent space into discrete code vectors with a separate learnable scalar codebook per dimension and (ii) applying strong model regularization via an unusually high weight decay. Intuitively, the latent space design forces the encoder to combinatorially construct codes from a small number of distinct scalar values, which in turn enables the decoder to assign a consistent meaning to each value. Regularization then serves to drive the model towards this parsimonious strategy. We demonstrate the broad applicability of this approach by adding it to both basic data-reconstructing (vanilla autoencoder) and latent-reconstructing (InfoGAN) generative models. For reliable evaluation, we also propose InfoMEC, a new set of metrics for disentanglement that is cohesively grounded in information theory and fixes well-established shortcomings in previous metrics. Together with regularization, latent quantization dramatically improves the modularity and explicitness of learned representations on a representative suite of benchmark datasets. In particular, our quantized-latent autoencoder (QLAE) consistently outperforms strong methods from prior work in these key disentanglement properties without compromising data reconstruction.","url_abs":"https://arxiv.org/abs/2305.18378v4","url_pdf":"https://arxiv.org/pdf/2305.18378v4.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":"disentanglement-via-latent-quantization-1","repo_url":"https://github.com/kylehkhsu/latent_quantization","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"jax","reach":null}],"tasks":[{"task_slug":"disentanglement","task_name":"Disentanglement"},{"task_slug":"inductive-bias","task_name":"Inductive Bias"},{"task_slug":"quantization","task_name":"Quantization"},{"task_slug":"representation-learning","task_name":"Representation Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2305.18378","atlas_url":"https://app.syntology.ai/?focus=2305.18378","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.18378"}},"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/kylehkhsu/latent_quantization","reach":null}],"summary":{"ran_fixture":1,"unverified":2},"by_repo_kind":{"official":{"samples":3,"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":0,"samples":[{"code_sha256_prefix":"68ec2a7d57a8d726","entry":"compute_nmi","repo":"kylehkhsu/latent_quantization","repo_kind":"official","path":"disentangle/metrics/infomec.py","file_url":"https://github.com/kylehkhsu/latent_quantization/blob/HEAD/disentangle/metrics/infomec.py","link_basis":"first_harvest_node","language":"python","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"68ec2a7d57a8d726"}},{"code_sha256_prefix":"6011e58708e92cba","entry":"compute_infoe","repo":"kylehkhsu/latent_quantization","repo_kind":"official","path":"disentangle/metrics/infomec.py","file_url":"https://github.com/kylehkhsu/latent_quantization/blob/HEAD/disentangle/metrics/infomec.py","link_basis":"first_harvest_node","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":"6011e58708e92cba"}},{"code_sha256_prefix":"1043f8c5c44231dc","entry":"process_sources","repo":"kylehkhsu/latent_quantization","repo_kind":"official","path":"disentangle/metrics/infomec.py","file_url":"https://github.com/kylehkhsu/latent_quantization/blob/HEAD/disentangle/metrics/infomec.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":"HONOURS","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"1043f8c5c44231dc"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}