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Guided Image Generation with Conditional Invertible Neural Networks

arXiv 1907.02392 ↗First posted 4 Jul 2019cs.CV cs.LGcited by 147

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Abstract (arXiv)

In this work, we address the task of natural image generation guided by a conditioning input. We introduce a new architecture called conditional invertible neural network (cINN). The cINN combines the purely generative INN model with an unconstrained feed-forward network, which efficiently preprocesses the conditioning input into useful features. All parameters of the cINN are jointly optimized with a stable, maximum likelihood-based training procedure. By construction, the cINN does not experience mode collapse and generates diverse samples, in contrast to e.g. cGANs. At the same time our model produces sharp images since no reconstruction loss is required, in contrast to e.g. VAEs. We demonstrate these properties for the tasks of MNIST digit generation and image colorization. Furthermore, we take advantage of our bi-directional cINN architecture to explore and manipulate emergent properties of the latent space, such as changing the image style in an intuitive way.

Key facts. Cited by 147 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Jul 2019.

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Citing papers per year: 2021: 34; 2022: 33; 2023: 29; 2024: 29; 2025: 11; 2026: 11.

147 papers.

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  1. 2021Palette: Image-to-Image Diffusion Modelscited by 1,296
  2. 2022Improving Diffusion Models for Inverse Problems using Manifold Constraintscited by 501
  3. 2021CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flowscited by 320
  4. 2022CDDFuse: Correlation-Driven Dual-Branch Feature Decomposition for Multi-Modality Image Fusioncited by 222
  5. 2021Low-Light Image Enhancement with Normalizing Flowcited by 184
  6. 2021Fully Convolutional Cross-Scale-Flows for Image-based Defect Detectioncited by 117
  7. 2022Asymmetric Student-Teacher Networks for Industrial Anomaly Detectioncited by 115
  8. 2021Colorization Transformercited by 89
  9. 2021Probabilistic Monocular 3D Human Pose Estimation with Normalizing Flowscited by 84
  10. 2021Hierarchical Conditional Flow: A Unified Framework for Image Super-Resolution and Image Rescalingcited by 73
  11. 2022Explicit Boundary Guided Semi-Push-Pull Contrastive Learning for Supervised Anomaly Detectioncited by 72
  12. 2021Invertible Denoising Network: A Light Solution for Real Noise Removalcited by 68
38 more
  1. 2022Towards Blind Watermarking: Combining Invertible and Non-invertible Mechanismscited by 61
  2. 2021Stochastic Image-to-Video Synthesis using cINNscited by 49
  3. 2021Detecting Model Misspecification in Amortized Bayesian Inference with Neural Networkscited by 48
  4. 2023JANA: Jointly Amortized Neural Approximation of Complex Bayesian Modelscited by 41
  5. 2022DDColor: Towards Photo-Realistic Image Colorization via Dual Decoderscited by 36
  6. 2024Conditional Wasserstein Distances with Applications in Bayesian OT Flow Matchingcited by 34
  7. 2021Stochastic Normalizing Flows for Inverse Problems: a Markov Chains Viewpointcited by 32
  8. 2021DeFlow: Learning Complex Image Degradations from Unpaired Data with Conditional Flowscited by 30
  9. 2024Hierarchical Gaussian Mixture Normalizing Flow Modeling for Unified Anomaly Detectioncited by 28
  10. 2022Generative Visual Prompt: Unifying Distributional Control of Pre-Trained Generative Modelscited by 27
  11. 2024ResAD: A Simple Framework for Class Generalizable Anomaly Detectioncited by 24
  12. 2021Generalized Normalizing Flows via Markov Chainscited by 23
  13. 2022PatchNR: Learning from Very Few Images by Patch Normalizing Flow Regularizationcited by 22
  14. 2021Generative Networks for Precision Enthusiastscited by 21
  15. 2022Detecting Multivariate Time Series Anomalies with Zero Known Labelcited by 21
  16. 2022Controllable Data Generation by Deep Learning: A Reviewcited by 20
  17. 2022An unfolding method based on conditional Invertible Neural Networks (cINN) using iterative trainingcited by 20
  18. 2024The Landscape of Unfolding with Machine Learningcited by 20
  19. 2021Noise Conditional Flow Model for Learning the Super-Resolution Spacecited by 19
  20. 2022Learning to Immunize Images for Tamper Localization and Self-Recoverycited by 19
  21. 2024Efficient mapping of phase diagrams with conditional Boltzmann Generatorscited by 18
  22. 2021PU-Flow: a Point Cloud Upsampling Network with Normalizing Flowscited by 17
  23. 2023EPiC-ly Fast Particle Cloud Generation with Flow-Matching and Diffusioncited by 17
  24. 2021Go with the Flows: Mixtures of Normalizing Flows for Point Cloud Generation and Reconstructioncited by 16
  25. 2023PanFlowNet: A Flow-Based Deep Network for Pan-sharpeningcited by 16
  26. 2021Inverse design of two-dimensional materials with invertible neural networkscited by 15
  27. 2022Invertible Rescaling Network and Its Extensionscited by 15
  28. 2023Label-Free Multivariate Time Series Anomaly Detectioncited by 14
  29. 2021Mitigating Generation Shifts for Generalized Zero-Shot Learningcited by 13
  30. 2021IICNet: A Generic Framework for Reversible Image Conversioncited by 13
  31. 2022Conditional Injective Flows for Bayesian Imagingcited by 13
  32. 2022GSMFlow: Generation Shifts Mitigating Flow for Generalized Zero-Shot Learningcited by 12
  33. 2022Imperceptible Adversarial Attack via Invertible Neural Networkscited by 12
  34. 2023Conditional Generative Models are Provably Robust: Pointwise Guarantees for Bayesian Inverse Problemscited by 12
  35. 2024Amortized Bayesian Multilevel Modelscited by 12
  36. 2022nu-Flows: Conditional Neutrino Regressioncited by 11
  37. 2021Normalizing Flow as a Flexible Fidelity Objective for Photo-Realistic Super-resolutioncited by 10
  38. 2023Normalizing Flow Ensembles for Rich Aleatoric and Epistemic Uncertainty Modelingcited by 10

50 most cited of 147 All 147 (JSON)