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arXivGANs & VAEs

Empirical Variational Autoencoder

Empirical Variational Autoencoder learns autoregressive latent priors empirically via one linear layer to close the VAE prior-posterior gap, yielding high-fidelity sequential generation competitive with diffusion models at much faster inference.

Kaede Shiohara

Published Oct 5, 2026 · ▲ 7 on Hugging Face · Code ★ 4

100% Readers1 of 1 upvoted
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AI panel: 6 of 20 reviewers recommend it
lenient 3/5
medium 3/10
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86%Must read
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arXivGANs & VAEs

Learning Discriminative Geometry for Drifting Models

Drifting models suffer from poor pixel-space performance because representation geometry controls KDE sample weighting; persistent representation learning learns discriminative geometry from raw pixels, cutting FID by 82, 95% without pretrained encoders.

Doudou Zhang, Wenwen Hou, Yilin Chen, Qi Chen

Published Oct 3, 2026 · ▲ 4 on Hugging Face · Code ★ 3

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AI panel: 14 of 20 reviewers recommend it
lenient 3/5
medium 9/10
strict 2/5
57%Worth a look
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NeurIPS 2026U BristolTencent Media LabU CardiffGANs & VAEs

PerQ: Inverse Generative Modeling for Neural Image Compression via Quantization Error Compensation

Tianhao Peng, Ho Man Kwan, Fan Zhang, Shan Liu and 1 more

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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57%Worth a look
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NeurIPS 2026The Ohio StateOhio State University, ColumbusGANs & VAEs

Self-Consuming Generative Models with Co-Evolving Human Preferences

Xiukun Wei, Tian Xie, Ding Zhu, Xueru Zhang

Atlanta Poster Session 6, Fri, Dec 11, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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45%Niche pick
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NeurIPS 2026École Normale Supérieure - ParisÉcole Normale SupérieureEcole Normale Supérieure de PariEcole Normale Supérieure, Ecole GANs & VAEs

The balance between feature learning and collapse in generative dynamical systems

Julian Brandon, Bruno Loureiro, N Alex Cayco Gajic, Arthur Pellegrino

Paris Poster Session 6, Fri, Dec 11, 2:30 PM–4:30 PM, Paris Poster Hall · Published 2026

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NeurIPS 2026BrownModal LabsIndependent ResearcherGANs & VAEs

Magnitude-preserving Layers Enable Efficient GANs

Nick Huang, Jackson Woodleigh, Aaron Gokaslan, Xinjie Yi and 1 more

Atlanta Poster Session 2, Wed, Dec 9, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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NeurIPS 2026The Ohio StateOhio State University, ColumbusGANs & VAEs

Stability and Diversity of Networked Self-Consuming Generative Ecosystems

Xiukun Wei, Yang Zhang, Xueru Zhang

Atlanta Poster Session 3, Thu, Dec 10, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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NeurIPS 2026National University of SingaporeGANs & VAEs

Transporting Quantiles to Codebook: Scalable Vector Quantization without Codebook Collapse

Yuwei Zeng, Zekun Shi

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026The University of ManchesterU ManchesterGANs & VAEs

Inter-domain Inference for Gaussian Process Variational Autoencoders

Xinxing Shi, Xiaoyu Jiang, Mauricio A Álvarez

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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57%Worth a look
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NeurIPS 2026The Australian NationalGANs & VAEs

ImageNet FID is a Pass Check, Not a Finish Line

Qinyu Zhao, Guangting Zheng, Caixia Zhou

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Mines Paris & École Normale Mines ParisTechGANs & VAEs

A Critical $\beta$-Scale for Posterior Collapse in Dirichlet $\beta$-VAEs

Delphine Doutsas, Bruno Figliuzzi

Paris Poster Session 1, Wed, Dec 9, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026

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NeurIPS 2026Korea Advanced Institute of ScieSeoul NationalGANs & VAEs

Discriminative Score Function: Turning Pretrained Models into Functional Generative Priors

Junhoo Lee, Hyeonjin Kim, Sangbum Han, Nojun Kwak

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Electronics and TelecommunicatioKorea Advanced Institute of ScieKorea AI Safety Institute, ETRIETRI (Electronics and TelecommunKorea Advanced Institute of ScieGANs & VAEs

Stability-Weighted Direction Regularization Disentangles Generator Shortcuts from Detection Signal

Jang Ho Choi, Seongho Kim, Jaehyun Choi, Dahye Kim and 2 more

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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83%Must read
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NeurIPS 2026GenentechYaleGANs & VAEs

Generate in Reconstruction Space, Match in Semantic Space: Transport Geometry for One-Step Generation

Matching in self-supervised feature spaces improves one-step generation via compact geometry, reducing ImageNet FID by 39x and revealing metric hacking risks.

Hugues Van Assel, Edward De Brouwer, Saeed Saremi, Gabriele Scalia and 1 more

Paris Poster Session 4, Thu, Dec 10, 5:30 PM–7:30 PM, Paris Poster Hall · Published 2026

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13/20 AI panelreviewers recommend it

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AI panel: 13 of 20 reviewers recommend it
lenient 3/5
medium 8/10
strict 2/5
72%Highly rated
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NeurIPS 2026ENS ParisGANs & VAEs

Disentanglement as Identifiable Pushforward Factorisation

Disentanglement is defined as pushforward factorization into one-dimensional seam factors, proven identifiable via Jacobian SVD conditions and linked to β-VAE diagonal posteriors.

Carl Allen

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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AI panel: 8 of 20 reviewers recommend it
lenient 2/5
medium 5/10
strict 1/5
76%Highly rated
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NeurIPS 2026Poznan University of TechnologyGANs & VAEs

Improving Generative Adversarial Networks with Self-Distillation

Self-Distilled GAN uses an EMA teacher generator to guide the active student via perceptual loss, improving FID, stabilizing training, and reducing parasitic cycling.

Antoni Nowinowski, Krzysztof Krawiec

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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AI panel: 10 of 20 reviewers recommend it
lenient 4/5
medium 5/10
strict 1/5
76%Highly rated
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NeurIPS 2026Google DeepMindGoogle DeepmindDeepMindGoogle Deep MindGoogleGANs & VAEs

MIND: Monge Inception Distance for Generative Models Evaluation

MIND uses sliced Wasserstein distance via sorting to evaluate generative models with 10x better sample efficiency, 100x faster computation, and greater adversarial robustness than FID.

Quentin Berthet, Clement CREPY, Romuald Elie, Klaus Greff and 2 more

Paris Poster Session 6, Fri, Dec 11, 2:30 PM–4:30 PM, Paris Poster Hall · Published 2026

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AI panel: 10 of 20 reviewers recommend it
lenient 4/5
medium 6/10
strict 0/5