mlsanityData to 7 Oct 2026

Adaptive Consensus Optimization Method for GANs

arXiv 2304.10317 ↗First posted 20 Apr 2023cs.LG cs.CV cs.NAcited by 3

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Abstract (arXiv)

We propose a second order gradient based method with ADAM and RMSprop for the training of generative adversarial networks. The proposed method is fastest to obtain similar accuracy when compared to prominent second order methods. Unlike state-of-the-art recent methods, it does not require solving a linear system, or it does not require additional mixed second derivative terms. We derive the fixed point iteration corresponding to proposed method, and show that the proposed method is convergent. The proposed method produces better or comparable inception scores, and comparable quality of images compared to other recently proposed state-of-the-art second order methods. Compared to first order methods such as ADAM, it produces significantly better inception scores. The proposed method is compared and validated on popular datasets such as FFHQ, LSUN, CIFAR10, MNIST, and Fashion MNIST for image generation tasksfootnoteAccepted in IJCNN 2023. Codes: urlhttps://github.com/misterpawan/acom

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 26 of its 58 references that are in the map. First posted on arXiv 20 Apr 2023.

Builds on

26 of 58 references.

References · 26

  1. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  2. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,326
  3. 2022Generative Adversarial Networkscited by 5,603
  4. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,283
  5. 2017Improved Training of Wasserstein GANscited by 2,223
  6. 2015Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networkscited by 2,048
  7. 2015LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loopcited by 1,350
  8. 2018Which Training Methods for GANs do actually Converge?cited by 672
  9. 2023Scaling up GANs for Text-to-Image Synthesiscited by 492
  10. 2019Implicit Regularization in Deep Matrix Factorizationcited by 402
  11. 2017Implicit Regularization in Matrix Factorizationcited by 318
  12. 2017Learning with Opponent-Learning Awarenesscited by 218
14 more
  1. 2017Training GANs with Optimismcited by 217
  2. 2017Deep Decentralized Multi-task Multi-Agent Reinforcement Learning under Partial Observabilitycited by 143
  3. 2018The Mechanics of n-Player Differentiable Gamescited by 118
  4. 2017The Numerics of GANscited by 104
  5. 2017Implicit Regularization in Deep Learningcited by 90
  6. 2017Implicit Regularization in Nonconvex Statistical Estimation: Gradient Descent Converges Linearly for Phase Retrieval, Matrix Completion, and Blind Deconvolutioncited by 63
  7. 2018Banach Wasserstein GANcited by 56
  8. 2019Stochastic Mirror Descent on Overparameterized Nonlinear Models: Convergence, Implicit Regularization, and Generalizationcited by 40
  9. 2019On Finding Local Nash Equilibria (and Only Local Nash Equilibria) in Zero-Sum Gamescited by 35
  10. 2019Competitive Gradient Descentcited by 32
  11. 2017Sobolev GANcited by 29
  12. 2019Nonparametric Density Estimation & Convergence Rates for GANs under Besov IPM Lossescited by 22
  13. 2017Stabilizing Adversarial Nets With Prediction Methodscited by 14
  14. 2019Implicit competitive regularization in GANscited by 14

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 1; 2025: 0; 2026: 1.

3 papers.

  1. 2026SignMuon: Communication-Efficient Distributed Muon Optimizationcited by 2
  2. 2024A Gauss-Newton Approach for Min-Max Optimization in Generative Adversarial Networkscited by 1
  3. 2023Enhancing ML model accuracy for Digital VLSI circuits using diffusion models: A study on synthetic data generationcited by 0