Data to 7 Oct 2026

DeLiGAN : Generative Adversarial Networks for Diverse and Limited Data

arXiv 1706.02071 ↗First posted 7 Jun 2017cs.CVcited by 77

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Abstract (arXiv)

A class of recent approaches for generating images, called Generative Adversarial Networks (GAN), have been used to generate impressively realistic images of objects, bedrooms, handwritten digits and a variety of other image modalities. However, typical GAN-based approaches require large amounts of training data to capture the diversity across the image modality. In this paper, we propose DeLiGAN -- a novel GAN-based architecture for diverse and limited training data scenarios. In our approach, we reparameterize the latent generative space as a mixture model and learn the mixture model's parameters along with those of GAN. This seemingly simple modification to the GAN framework is surprisingly effective and results in models which enable diversity in generated samples although trained with limited data. In our work, we show that DeLiGAN can generate images of handwritten digits, objects and hand-drawn sketches, all using limited amounts of data. To quantitatively characterize intra-class diversity of generated samples, we also introduce a modified version of "inception-score", a measure which has been found to correlate well with human assessment of generated samples.

Key facts. Cited by 77 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Jun 2017.

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Citing papers per year: 2020: 1; 2021: 18; 2022: 20; 2023: 17; 2024: 7; 2025: 9; 2026: 5.

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2020DiffWave: A Versatile Diffusion Model for Audio Synthesiscited by 1,339
2021Large Scale Image Completion via Co-Modulated Generative Adversarial Networkscited by 219
2023InterDiff: Generating 3D Human-Object Interactions with Physics-Informed Diffusioncited by 181
2022It's Raw! Audio Generation with State-Space Modelscited by 171
2021VideoMoCo: Contrastive Video Representation Learning with Temporally Adversarial Examplescited by 154
2022BeLFusion: Latent Diffusion for Behavior-Driven Human Motion Predictioncited by 82
2023HumanMAC: Masked Motion Completion for Human Motion Predictioncited by 81
2023Efficient Dataset Distillation via Minimax Diffusioncited by 81
2023The Song Describer Dataset: a Corpus of Audio Captions for Music-and-Language Evaluationcited by 68
2021Generating Smooth Pose Sequences for Diverse Human Motion Predictioncited by 66
2022Data Augmentation techniques in time series domain: A survey and taxonomycited by 41
2022Can Push-forward Generative Models Fit Multimodal Distributions?cited by 41

38 more

2023Diverse Human Motion Prediction Guided by Multi-Level Spatial-Temporal Anchorscited by 34
2022A generic diffusion-based approach for 3D human pose prediction in the wildcited by 33
2022Diverse Human Motion Prediction via Gumbel-Softmax Sampling from an Auxiliary Spacecited by 32
2022Motron: Multimodal Probabilistic Human Motion Forecastingcited by 30
2022Human Joint Kinematics Diffusion-Refinement for Stochastic Motion Predictioncited by 28
2023Stochastic Multi-Person 3D Motion Forecastingcited by 28
2023CoMusion: Towards Consistent Stochastic Human Motion Prediction via Motion Diffusioncited by 21
2024Distribution-Aware Data Expansion with Diffusion Modelscited by 20
2022Textual Manifold-based Defense Against Natural Language Adversarial Examplescited by 19
2022SPARF: Large-Scale Learning of 3D Sparse Radiance Fields from Few Input Imagescited by 17
2022HiT-DVAE: Human Motion Generation via Hierarchical Transformer Dynamical VAEcited by 16
2022A Deep Generative Model for Feasible and Diverse Population Synthesiscited by 16
2023TransFusion: A Practical and Effective Transformer-based Diffusion Model for 3D Human Motion Predictioncited by 16
2025Gaussian Mixture Flow Matching Modelscited by 16
2022A survey on GANs for computer vision: Recent research, analysis and taxonomycited by 15
2021TrajeVAE: Controllable Human Motion Generation from Trajectoriescited by 14
2023Ten Years of Generative Adversarial Nets (GANs): A survey of the state-of-the-artcited by 14
2024Learning Semantic Latent Directions for Accurate and Controllable Human Motion Predictioncited by 14
2021A review of Generative Adversarial Networks (GANs) and its applications in a wide variety of disciplines -- From Medical to Remote Sensingcited by 13
2025Nonisotropic Gaussian Diffusion for Realistic 3D Human Motion Predictioncited by 12
2021Adversarial Imitation Learning with Trajectorial Augmentation and Correctioncited by 11
2021Partition-Guided GANscited by 9
2024A Survey on Deep Clustering: From the Prior Perspectivecited by 9
2022Visualizing Global Explanations of Point Cloud DNNscited by 8
2024MotionWavelet: Human Motion Prediction via Wavelet Manifold Learningcited by 8
2021Latent reweighting, an almost free improvement for GANscited by 6
2024Integration of Mixture of Experts and Multimodal Generative AI in Internet of Vehicles: A Surveycited by 6
2025ReCon: Region-Controllable Data Augmentation with Rectification and Alignment for Object Detectioncited by 6
2022A Realism Metric for Generated LiDAR Point Cloudscited by 5
2022Multi-level Latent Space Structuring for Generative Controlcited by 4
2023Disentanglement in a GAN for Unconditional Speech Synthesiscited by 4
2025A Survey on Music Generation from Single-Modal, Cross-Modal, and Multi-Modal Perspectivescited by 4
2025CacheFlow: Fast Human Motion Prediction by Cached Normalizing Flowcited by 4
2021Cluster-guided Image Synthesis with Unconditional Modelscited by 3
2022Adversarial Audio Synthesis with Complex-valued Polynomial Networkscited by 3
2022Unveiling the Latent Space Geometry of Push-Forward Generative Modelscited by 3
2022GAN You Hear Me? Reclaiming Unconditional Speech Synthesis from Diffusion Modelscited by 3
2023Understanding Deep Generative Models with Generalized Empirical Likelihoodscited by 3

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