Data to 14 Oct 2026

Dual Contradistinctive Generative Autoencoder

arXiv 2011.10063 ↗First posted 19 Nov 2020cs.CVcited by 58

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Cited by 23 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

We present a new generative autoencoder model with dual contradistinctive losses to improve generative autoencoder that performs simultaneous inference (reconstruction) and synthesis (sampling). Our model, named dual contradistinctive generative autoencoder (DC-VAE), integrates an instance-level discriminative loss (maintaining the instance-level fidelity for the reconstruction/synthesis) with a set-level adversarial loss (encouraging the set-level fidelity for there construction/synthesis), both being contradistinctive. Extensive experimental results by DC-VAE across different resolutions including 32x32, 64x64, 128x128, and 512x512 are reported. The two contradistinctive losses in VAE work harmoniously in DC-VAE leading to a significant qualitative and quantitative performance enhancement over the baseline VAEs without architectural changes. State-of-the-art or competitive results among generative autoencoders for image reconstruction, image synthesis, image interpolation, and representation learning are observed. DC-VAE is a general-purpose VAE model, applicable to a wide variety of downstream tasks in computer vision and machine learning.

Key facts. Cited by 58 papers in the map (data through 14 Oct 2026). First posted on arXiv 19 Nov 2020.

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Citing papers per year: 2021: 14; 2022: 9; 2023: 19; 2024: 9; 2025: 7.

58 papers.

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2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
2023Consistency Modelscited by 1,822
2021Vector-quantized Image Modeling with Improved VQGANcited by 658
2021Score-based Generative Modeling in Latent Spacecited by 636
2021Tackling the Generative Learning Trilemma with Denoising Diffusion GANscited by 516
2021Dataset Condensation with Distribution Matchingcited by 365
2021Score-Based Generative Modeling with Critically-Damped Langevin Diffusioncited by 233
2021ViTGAN: Training GANs with Vision Transformerscited by 110
2022DiffuseVAE: Efficient, Controllable and High-Fidelity Generation from Low-Dimensional Latentscited by 104
2023DataDAM: Efficient Dataset Distillation with Attention Matchingcited by 84
2022Frido: Feature Pyramid Diffusion for Complex Scene Image Synthesiscited by 75
2021On Aliased Resizing and Surprising Subtleties in GAN Evaluationcited by 51

38 more

2022DiRA: Discriminative, Restorative, and Adversarial Learning for Self-supervised Medical Image Analysiscited by 48
2021Generating Synthetic Mixed-type Longitudinal Electronic Health Records for Artificial Intelligent Applicationscited by 47
2022Maximum Likelihood Training of Implicit Nonlinear Diffusion Modelscited by 42
2023Generative Modeling through the Semi-dual Formulation of Unbalanced Optimal Transportcited by 41
2021Global Context with Discrete Diffusion in Vector Quantised Modelling for Image Generationcited by 40
2023Efficient-VQGAN: Towards High-Resolution Image Generation with Efficient Vision Transformerscited by 33
2024Improving Diffusion-Based Image Synthesis with Context Predictioncited by 32
2024Scalable Wasserstein Gradient Flow for Generative Modeling through Unbalanced Optimal Transportcited by 23
2023Learning Energy-Based Models by Cooperative Diffusion Recovery Likelihoodcited by 19
2023Catch Missing Details: Image Reconstruction with Frequency Augmented Variational Autoencodercited by 18
2021Closed-Loop Data Transcription to an LDR via Minimaxing Rate Reductioncited by 17
2023MaskDiffusion: Boosting Text-to-Image Consistency with Conditional Maskcited by 17
2023Semi-Implicit Denoising Diffusion Models (SIDDMs)cited by 14
2024Glauber Generative Model: Discrete Diffusion Models via Binary Classificationcited by 11
2021Beta-VAE Reproducibility: Challenges and Extensionscited by 10
2023Analyzing and Improving Optimal-Transport-based Adversarial Networkscited by 8
2022Unsupervised Learning of Structured Representations via Closed-Loop Transcriptioncited by 7
2023Diffusion Models with Deterministic Normalizing Flow Priorscited by 7
2021Scalable Variational Quantum Circuits for Autoencoder-based Drug Discoverycited by 6
2023Restoration based Generative Modelscited by 6
2023ACT-Diffusion: Efficient Adversarial Consistency Training for One-step Diffusion Modelscited by 5
2024Generative Modelling with High-Order Langevin Dynamicscited by 5
2024Entropy-Informed Weighting Channel Normalizing Flow for Deep Generative Modelscited by 5
2022DisPositioNet: Disentangled Pose and Identity in Semantic Image Manipulationcited by 4
2023Closed-Loop Transcription via Convolutional Sparse Codingcited by 4
2024Towards Controllable Time Series Generationcited by 4
2022FusionVAE: A Deep Hierarchical Variational Autoencoder for RGB Image Fusioncited by 3
2023Probabilistic and Semantic Descriptions of Image Manifolds and Their Applicationscited by 3
2023Energy-Calibrated VAE with Test Time Free Lunchcited by 3
2023The Challenges of Image Generation Models in Generating Multi-Component Imagescited by 3
2024MLVICX: Multi-Level Variance-Covariance Exploration for Chest X-ray Self-Supervised Representation Learningcited by 3
2025Denoising Multi-Beta VAE: Representation Learning for Disentanglement and Generationcited by 3
2025CoVAE: Consistency Training of Variational Autoencoderscited by 3
2022SPI-GAN: Denoising Diffusion GANs with Straight-Path Interpolationscited by 2
2023Spider GAN: Leveraging Friendly Neighbors to Accelerate GAN Trainingcited by 2
2025Learning Energy-Based Generative Models via Potential Flow: A Variational Principle Approach to Probability Density Homotopy Matchingcited by 2
2021Momentum Contrastive Autoencoder: Using Contrastive Learning for Latent Space Distribution Matching in WAEcited by 1
2022Super-resolution Reconstruction of Single Image for Latent featurescited by 1

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