mlsanityData to 7 Oct 2026

Landscape Learning for Neural Network Inversion

arXiv 2206.09027 ↗First posted 17 Jun 2022cs.CV cs.LGcited by 15

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Abstract (arXiv)

Many machine learning methods operate by inverting a neural network at inference time, which has become a popular technique for solving inverse problems in computer vision, robotics, and graphics. However, these methods often involve gradient descent through a highly non-convex loss landscape, causing the optimization process to be unstable and slow. We introduce a method that learns a loss landscape where gradient descent is efficient, bringing massive improvement and acceleration to the inversion process. We demonstrate this advantage on a number of methods for both generative and discriminative tasks, including GAN inversion, adversarial defense, and 3D human pose reconstruction.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). Builds on 56 of its 83 references that are in the map. First posted on arXiv 17 Jun 2022.

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56 of 83 references.

References · 56

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  2. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,029
  3. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,326
  4. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 5,986
  5. 2013Intriguing properties of neural networkscited by 5,114
  6. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,861
  7. 2021The Power of Scale for Parameter-Efficient Prompt Tuningcited by 3,769
  8. 2019Analyzing and Improving the Image Quality of StyleGANcited by 3,580
  9. 2013Playing Atari with Deep Reinforcement Learningcited by 3,211
  10. 2016Towards Evaluating the Robustness of Neural Networkscited by 2,976
  11. 2017Progressive Growing of GANs for Improved Quality, Stability, and Variationcited by 2,967
  12. 2021Plenoxels: Radiance Fields without Neural Networkscited by 1,607
38 more
  1. 2019Expressive Body Capture: 3D Hands, Face, and Body from a Single Imagecited by 1,600
  2. 2020Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attackscited by 1,442
  3. 2015LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loopcited by 1,350
  4. 2018Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examplescited by 1,259
  5. 2018On First-Order Meta-Learning Algorithmscited by 1,003
  6. 2020Encoding in Style: a StyleGAN Encoder for Image-to-Image Translationcited by 727
  7. 2019Interpreting the Latent Space of GANs for Semantic Face Editingcited by 691
  8. 2019Image2StyleGAN: How to Embed Images Into the StyleGAN Latent Space?cited by 675
  9. 2016Learning to learn by gradient descent by gradient descentcited by 616
  10. 2021Designing an Encoder for StyleGAN Image Manipulationcited by 542
  11. 2020Overfitting in adversarially robust deep learningcited by 504
  12. 2020In-Domain GAN Inversion for Real Image Editingcited by 420
  13. 2019Unlabeled Data Improves Adversarial Robustnesscited by 415
  14. 2016Input Convex Neural Networkscited by 407
  15. 2020GRAB: A Dataset of Whole-Body Human Grasping of Objectscited by 397
  16. 2020PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Modelscited by 358
  17. 2019Image2StyleGAN++: How to Edit the Embedded Images?cited by 356
  18. 2016Generative Visual Manipulation on the Natural Image Manifoldcited by 348
  19. 2017Compressed Sensing using Generative Modelscited by 337
  20. 2019Resolving 3D Human Pose Ambiguities with 3D Scene Constraintscited by 294
  21. 2022VQGAN-CLIP: Open Domain Image Generation and Editing with Natural Language Guidancecited by 285
  22. 2022Exploring Visual Prompts for Adapting Large-Scale Modelscited by 271
  23. 2018Probabilistic Model-Agnostic Meta-Learningcited by 254
  24. 2019On the "steerability" of generative adversarial networkscited by 247
  25. 2020Exploiting Deep Generative Prior for Versatile Image Restoration and Manipulationcited by 225
  26. 2021Learning to Optimize: A Primer and A Benchmarkcited by 206
  27. 2017Neural Style Transfer: A Reviewcited by 199
  28. 2016Semantic Image Inpainting with Deep Generative Modelscited by 156
  29. 2020Adversarial Latent Autoencoderscited by 155
  30. 2019Seeing What a GAN Cannot Generatecited by 148
  31. 2019Online Meta-Learningcited by 137
  32. 2020Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retrainingcited by 121
  33. 2016Invertible Conditional GANs for image editingcited by 118
  34. 2017Learned Optimizers that Scale and Generalizecited by 109
  35. 2021HyperInverter: Improving StyleGAN Inversion via Hypernetworkcited by 85
  36. 2019Metric Learning for Adversarial Robustnesscited by 77
  37. 2020Transforming and Projecting Images into Class-conditional Generative Networkscited by 76
  38. 2021FuseDream: Training-Free Text-to-Image Generation with Improved CLIP+GAN Space Optimizationcited by 71

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 3; 2023: 1; 2024: 4; 2025: 6; 2026: 1.

15 papers.

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  1. 2022Understanding Zero-Shot Adversarial Robustness for Large-Scale Modelscited by 117
  2. 2024pix2gestalt: Amodal Segmentation by Synthesizing Wholescited by 70
  3. 2022FLEX: Full-Body Grasping Without Full-Body Graspscited by 46
  4. 2024Differentiable Robot Renderingcited by 20
  5. 2024Network Inversion of Convolutional Neural Netscited by 8
  6. 2023Regularizing Neural Networks with Meta-Learning Generative Modelscited by 5
  7. 2025Shortcut Learning Susceptibility in Vision Classifierscited by 4
  8. 2022Adversarially Robust Video Perception by Seeing Motioncited by 2
  9. 2024Network Inversion for Training-Like Data Reconstructioncited by 1
  10. 2025Network Inversion for Uncertainty-Aware Out-of-Distribution Detectioncited by 1
  11. 2025Investigating the Invertibility of Multimodal Latent Spaces: Limitations of Optimization-Based Methodscited by 1
  12. 2025Activation Matching for Explanation Generationcited by 1
3 more
  1. 2025EXP-CAM: Explanation Generation and Circuit Discovery Using Classifier Activation Matchingcited by 0
  2. 2025TIE: A Training-Inversion-Exclusion Framework for Visually Interpretable and Uncertainty-Guided Out-of-Distribution Detectioncited by 0
  3. 2026PTP: Previous-Token Prediction based LLM Inversion for Near-Exact Prompt Reconstructioncited by 0