Disentanglement

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

3 leaderboard tables shown for this task, 3 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

13 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

30 shown of 744 papers with code (1,854 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• A Style-Based Generator Architecture for Generative Adversarial Networks
  12 Dec 2018 83 repositories listed Syntology ran 226 of 396 samples · 170 unverified · 229 pointer-only (licence)
  We propose an alternative generator architecture for generative adversarial networks, borrowing from style transfer literature.
• Disentangling by Factorising
  16 Feb 2018 17 repositories listed Syntology ran 10 of 24 samples · 14 unverified · 6 pointer-only (licence)
  We define and address the problem of unsupervised learning of disentangled representations on data generated from independent factors of variation.
• Adversarial Latent Autoencoders
  9 Apr 2020 12 repositories listed Syntology ran 0 of 2 samples · 2 unverified
  We designed two autoencoders: one based on a MLP encoder, and another based on a StyleGAN generator, which we call StyleALAE.
• Sigmoid Loss for Language Image Pre-Training
  27 Mar 2023 11 repositories listed Syntology ran 12 of 29 samples · 17 unverified · 25 pointer-only (licence)
  We propose a simple pairwise Sigmoid loss for Language-Image Pre-training (SigLIP).
• Isolating Sources of Disentanglement in Variational Autoencoders
  14 Feb 2018 10 repositories listed Syntology ran 4 of 9 samples · 5 unverified
  We decompose the evidence lower bound to show the existence of a term measuring the total correlation between latent variables.
• Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations
  29 Nov 2018 8 repositories listed Syntology ran 3 of 3 samples · 0 unverified · 1 pointer-only (licence)
  The key idea behind the unsupervised learning of disentangled representations is that real-world data is generated by a few explanatory factors of variation which can be recovered by unsupervised learning algorithms.
• beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework
  26 Apr 2017 6 repositories listed
  Learning an interpretable factorised representation of the independent data generative factors of the world without supervision is an important precursor for the development of artificial intelligence that is able to…
• LEO: Generative Latent Image Animator for Human Video Synthesis
  6 May 2023 5 repositories listed
  Our key idea is to represent motion as a sequence of flow maps in the generation process, which inherently isolate motion from appearance.
• Learning concise representations for regression by evolving networks of trees
  3 Jul 2018 5 repositories listed
  We propose and study a method for learning interpretable representations for the task of regression.
• Multiple Heads are Better than One: Few-shot Font Generation with Multiple Localized Experts
  2 Apr 2021 4 repositories listed
  MX-Font extracts multiple style features not explicitly conditioned on component labels, but automatically by multiple experts to represent different local concepts, e.
• Stylized Neural Painting
  16 Nov 2020 4 repositories listed
  Different from previous image-to-image translation methods that formulate the translation as pixel-wise prediction, we deal with such an artistic creation process in a vectorized environment and produce a sequence of…
• ControlVAE: Tuning, Analytical Properties, and Performance Analysis
  31 Oct 2020 4 repositories listed
  ControlVAE is a new variational autoencoder (VAE) framework that combines the automatic control theory with the basic VAE to stabilize the KL-divergence of VAE models to a specified value.
• Measuring the Biases and Effectiveness of Content-Style Disentanglement
  27 Aug 2020 4 repositories listed
  In this paper, we conduct an empirical study to investigate the role of different biases in content-style disentanglement settings and unveil the relationship between the degree of disentanglement and task performance.
• Disentangled and Controllable Face Image Generation via 3D Imitative-Contrastive Learning
  24 Apr 2020 4 repositories listed Syntology ran 1 of 12 samples · 11 unverified
  Our method can also be used to embed real images into the disentangled latent space.
• Interpreting the Latent Space of GANs for Semantic Face Editing
  25 Jul 2019 4 repositories listed Syntology ran 2 of 5 samples · 3 unverified · 5 pointer-only (licence)
  In this work, we propose a novel framework, called InterFaceGAN, for semantic face editing by interpreting the latent semantics learned by GANs.
• On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset
  7 Jun 2019 4 repositories listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
  Learning meaningful and compact representations with disentangled semantic aspects is considered to be of key importance in representation learning.
• StyleCrafter: Enhancing Stylized Text-to-Video Generation with Style Adapter
  1 Dec 2023 3 repositories listed Syntology ran 13 of 23 samples · 10 unverified
  To address these challenges, we introduce StyleCrafter, a generic method that enhances pre-trained T2V models with a style control adapter, enabling video generation in any style by providing a reference image.
• ProSpect: Prompt Spectrum for Attribute-Aware Personalization of Diffusion Models
  25 May 2023 3 repositories listed Syntology ran 12 of 13 samples · 1 unverified
  We apply ProSpect in various personalized attribute-aware image generation applications, such as image-guided or text-driven manipulations of materials, style, and layout, achieving previously unattainable results from…
• Versatile Diffusion: Text, Images and Variations All in One Diffusion Model
  15 Nov 2022 3 repositories listed
  In this work, we expand the existing single-flow diffusion pipeline into a multi-task multimodal network, dubbed Versatile Diffusion (VD), that handles multiple flows of text-to-image, image-to-text, and variations in…
• Exploring Video Quality Assessment on User Generated Contents from Aesthetic and Technical Perspectives
  9 Nov 2022 3 repositories listed Syntology ran 3 of 3 samples · 0 unverified · 3 pointer-only (licence)
  In light of this, we propose the Disentangled Objective Video Quality Evaluator (DOVER) to learn the quality of UGC videos based on the two perspectives.
• Representation Topology Divergence: A Method for Comparing Neural Network Representations
  31 Dec 2021 3 repositories listed Syntology ran 1 of 7 samples · 6 unverified · 2 pointer-only (licence)
  Comparison of data representations is a complex multi-aspect problem that has not enjoyed a complete solution yet.
• Volume Rendering of Neural Implicit Surfaces
  22 Jun 2021 3 repositories listed Syntology ran 5 of 9 samples · 4 unverified
  Accurate sampling is important to provide a precise coupling of geometry and radiance; and (iii) it allows efficient unsupervised disentanglement of shape and appearance in volume rendering.
• Face Identity Disentanglement via Latent Space Mapping
  15 May 2020 3 repositories listed
  Learning disentangled representations of data is a fundamental problem in artificial intelligence.
• Disentangling Physical Dynamics from Unknown Factors for Unsupervised Video Prediction
  3 Mar 2020 3 repositories listed Syntology ran 0 of 1 samples · 1 unverified
  Leveraging physical knowledge described by partial differential equations (PDEs) is an appealing way to improve unsupervised video prediction methods.
• Weakly-Supervised Disentanglement Without Compromises
  7 Feb 2020 3 repositories listed
  Third, we perform a large-scale empirical study and show that such pairs of observations are sufficient to reliably learn disentangled representations on several benchmark data sets.
• MixNMatch: Multifactor Disentanglement and Encoding for Conditional Image Generation
  26 Nov 2019 3 repositories listed
  We present MixNMatch, a conditional generative model that learns to disentangle and encode background, object pose, shape, and texture from real images with minimal supervision, for mix-and-match image generation.
• Variational Autoencoders and Nonlinear ICA: A Unifying Framework
  10 Jul 2019 3 repositories listed Syntology ran 1 of 17 samples · 16 unverified · 1 pointer-only (licence)
  We address this issue by showing that for a broad family of deep latent-variable models, identification of the true joint distribution over observed and latent variables is actually possible up to very simple…
• Deep Music Analogy Via Latent Representation Disentanglement
  9 Jun 2019 3 repositories listed
  Analogy-making is a key method for computer algorithms to generate both natural and creative music pieces.
• Multiple-Attribute Text Style Transfer
  1 Nov 2018 3 repositories listed
  The dominant approach to unsupervised "style transfer" in text is based on the idea of learning a latent representation, which is independent of the attributes specifying its "style".
• A Large-Scale Corpus for Conversation Disentanglement
  25 Oct 2018 3 repositories listed
  Disentangling conversations mixed together in a single stream of messages is a difficult task, made harder by the lack of large manually annotated datasets.

Syntology lines on 16 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.