mlsanityData to 7 Oct 2026

AdvNF: Reducing Mode Collapse in Conditional Normalising Flows using Adversarial Learning

arXiv 2401.15948 ↗First posted 29 Jan 2024cs.LG cond-mat.stat-mech physics.comp-phcited by 6

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Abstract (arXiv)

Deep generative models complement Markov-chain-Monte-Carlo methods for efficiently sampling from high-dimensional distributions. Among these methods, explicit generators, such as Normalising Flows (NFs), in combination with the Metropolis Hastings algorithm have been extensively applied to get unbiased samples from target distributions. We systematically study central problems in conditional NFs, such as high variance, mode collapse and data efficiency. We propose adversarial training for NFs to ameliorate these problems. Experiments are conducted with low-dimensional synthetic datasets and XY spin models in two spatial dimensions.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 30 of its 62 references that are in the map. First posted on arXiv 29 Jan 2024.

Builds on

30 of 62 references.

References · 30

  1. 2022Generative Adversarial Networkscited by 5,603
  2. 2015Variational Inference with Normalizing Flowscited by 2,161
  3. 2014Conditional Generative Adversarial Netscited by 2,152
  4. 2016Density estimation using Real NVPcited by 2,029
  5. 2019Normalizing Flows for Probabilistic Modeling and Inferencecited by 1,315
  6. 2019An Introduction to Variational Autoencoderscited by 997
  7. 2016NIPS 2016 Tutorial: Generative Adversarial Networkscited by 284
  8. 2020A Review on Generative Adversarial Networks: Algorithms, Theory, and Applicationscited by 209
  9. 2017VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learningcited by 199
  10. 2019Learning Likelihoods with Conditional Normalizing Flowscited by 170
  11. 2020Stochastic Normalizing Flowscited by 156
  12. 2019Flow-based generative models for Markov chain Monte Carlo in lattice field theorycited by 147
18 more
  1. 2019Mode Seeking Generative Adversarial Networks for Diverse Image Synthesiscited by 138
  2. 2016Mode Regularized Generative Adversarial Networkscited by 123
  3. 2020Equivariant flow-based sampling for lattice gauge theorycited by 102
  4. 2017A Generative Model For Zero Shot Learning Using Conditional Variational Autoencoderscited by 64
  5. 2017Unsupervised learning of phase transitions: from principal component analysis to variational autoencoderscited by 47
  6. 2021Resampling Base Distributions of Normalizing Flowscited by 42
  7. 2021Greedification Operators for Policy Optimization: Investigating Forward and Reverse KL Divergencescited by 40
  8. 2021Normalizing flows for atomic solidscited by 39
  9. 2013Deep Generative Stochastic Networks Trainable by Backpropcited by 36
  10. 2022Evaluating generative models in high energy physicscited by 31
  11. 2021Flow-based sampling for fermionic lattice field theoriescited by 27
  12. 2021Flow-based sampling for multimodal and extended-mode distributions in lattice field theorycited by 25
  13. 2018MGGAN: Solving Mode Collapse using Manifold Guided Trainingcited by 22
  14. 2022Flow-based sampling in the lattice Schwinger model at criticalitycited by 20
  15. 2018Physics-informed deep generative modelscited by 16
  16. 2021Machine Learning of Thermodynamic Observables in the Presence of Mode Collapsecited by 10
  17. 2020Self-Supervised Learning of Generative Spin-Glasses with Normalizing Flowscited by 2
  18. 2017Towards meaningful physics from generative modelscited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 3; 2026: 3.

6 papers.

  1. 2025Physics-Conditioned Diffusion Models for Lattice Gauge Theorycited by 9
  2. 2025SCORENF: Score-based Normalizing Flows for Sampling Unnormalized distributionscited by 2
  3. 2025MAGIC-Flow: Multiscale Adaptive Conditional Flows for Generation and Interpretable Classificationcited by 0
  4. 2026Accurate Sampling from Diffusion Modelscited by 0
  5. 2026SIMANF: Sample Free Learning of Unnormalized Distributions via Simulated Annealing in Normalizing Flowscited by 0
  6. 2026FUND: Density Flow for Sampling Unnormalised Distributionscited by 0