mlsanityData to 14 Oct 2026

AdaGAN: Boosting Generative Models

arXiv 1701.02386 ↗First posted 9 Jan 2017stat.ML cs.LGcited by 34

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Abstract (arXiv)

Generative Adversarial Networks (GAN) (Goodfellow et al., 2014) are an effective method for training generative models of complex data such as natural images. However, they are notoriously hard to train and can suffer from the problem of missing modes where the model is not able to produce examples in certain regions of the space. We propose an iterative procedure, called AdaGAN, where at every step we add a new component into a mixture model by running a GAN algorithm on a reweighted sample. This is inspired by boosting algorithms, where many potentially weak individual predictors are greedily aggregated to form a strong composite predictor. We prove that such an incremental procedure leads to convergence to the true distribution in a finite number of steps if each step is optimal, and convergence at an exponential rate otherwise. We also illustrate experimentally that this procedure addresses the problem of missing modes.

Key facts. Cited by 34 papers in the map (data through 14 Oct 2026). First posted on arXiv 9 Jan 2017.

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Citing papers per year: 2018: 1; 2019: 0; 2020: 0; 2021: 14; 2022: 4; 2023: 8; 2024: 2; 2025: 2; 2026: 3.

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  1. 2021MG-GAN: A Multi-Generator Model Preventing Out-of-Distribution Samples in Pedestrian Trajectory Predictioncited by 61
  2. 2023Synthetic data, real errors: how (not) to publish and use synthetic datacited by 29
  3. 2023Compositional Sculpting of Iterative Generative Processescited by 27
  4. 2018MEGAN: Mixture of Experts of Generative Adversarial Networks for Multimodal Image Generationcited by 15
  5. 2023Ten Years of Generative Adversarial Nets (GANs): A survey of the state-of-the-artcited by 14
  6. 2021Adversarial sampling of unknown and high-dimensional conditional distributionscited by 13
  7. 2021Influence Estimation for Generative Adversarial Networkscited by 11
  8. 2021MCL-GAN: Generative Adversarial Networks with Multiple Specialized Discriminatorscited by 11
  9. 2021Ensembles of GANs for synthetic training data generationcited by 6
  10. 2022A Realism Metric for Generated LiDAR Point Cloudscited by 5
  11. 2023MotionVideoGAN: A Novel Video Generator Based on the Motion Space Learned from Image Pairscited by 5
  12. 2024Adversarial Imitation Learning via Boostingcited by 5
22 more
  1. 2021Generative Adversarial Networks and Adversarial Autoencoders: Tutorial and Surveycited by 4
  2. 2021On Characterizing GAN Convergence Through Proximal Duality Gapcited by 3
  3. 2021Training Wasserstein GANs without gradient penaltiescited by 3
  4. 2021Overcoming Mode Collapse with Adaptive Multi Adversarial Trainingcited by 3
  5. 2022Revisiting GANs by Best-Response Constraint: Perspective, Methodology, and Applicationcited by 3
  6. 2023A hybrid quantum-classical conditional generative adversarial network algorithm for human-centered paradigm in cloudcited by 3
  7. 2021Leverage Score Sampling for Complete Mode Coverage in Generative Adversarial Networkscited by 2
  8. 2021Quantifying point cloud realism through adversarially learned latent representationscited by 2
  9. 2023Spider GAN: Leveraging Friendly Neighbors to Accelerate GAN Trainingcited by 2
  10. 2022Why GANs are overkill for NLPcited by 1
  11. 2024NRGBoost: Energy-Based Generative Boosted Treescited by 1
  12. 2025Beyond a Single Mode: GAN Ensembles for Diverse Medical Data Generationcited by 1
  13. 2021Ebola Optimization Search Algorithm (EOSA): A new metaheuristic algorithm based on the propagation model of Ebola virus diseasecited by 0
  14. 2021VARGAN: Variance Enforcing Network Enhanced GANcited by 0
  15. 2021GM Score: Incorporating inter-class and intra-class generator diversity, discriminability of disentangled representation, and sample fidelity for evaluating GANscited by 0
  16. 2022Distribution Fitting for Combating Mode Collapse in Generative Adversarial Networkscited by 0
  17. 2023Anomaly Detection with Ensemble of Encoder and Decodercited by 0
  18. 2023A New Paradigm for Generative Adversarial Networks based on Randomized Decision Rulescited by 0
  19. 2025PENEX: AdaBoost-Inspired Neural Network Regularizationcited by 0
  20. 2026Generating High-quality Privacy-preserving Synthetic Datacited by 0
  21. 2026Enhanced Low-Density Region Exploration in Classifier-Guided Diffusion Models Through Modified Reverse Diffusion Samplingcited by 0
  22. 2026Mixture of Decoders for Diverse Dialog Response Generationcited by 0