mlsanityData to 14 Oct 2026

Procedural Image Programs for Representation Learning

arXiv 2211.16412 ↗First posted 29 Nov 2022cs.CV cs.LGcited by 28

Not on the map. Why?

Abstract (arXiv)

Learning image representations using synthetic data allows training neural networks without some of the concerns associated with real images, such as privacy and bias. Existing work focuses on a handful of curated generative processes which require expert knowledge to design, making it hard to scale up. To overcome this, we propose training with a large dataset of twenty-one thousand programs, each one generating a diverse set of synthetic images. These programs are short code snippets, which are easy to modify and fast to execute using OpenGL. The proposed dataset can be used for both supervised and unsupervised representation learning, and reduces the gap between pre-training with real and procedurally generated images by 38%.

Key facts. Cited by 28 papers in the map (data through 14 Oct 2026). Builds on 31 of its 48 references that are in the map. First posted on arXiv 29 Nov 2022.

Builds on

31 of 48 references.

References · 31

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  3. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 12,049
  4. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,177
  5. 2018Representation Learning with Contrastive Predictive Codingcited by 7,777
  6. 2021Masked Autoencoders Are Scalable Vision Learnerscited by 7,403
  7. 2017mixup: Beyond Empirical Risk Minimizationcited by 5,183
  8. 2019Analyzing and Improving the Image Quality of StyleGANcited by 3,588
  9. 2019CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Featurescited by 2,884
  10. 2020Supervised Contrastive Learningcited by 2,829
  11. 2020RAFT: Recurrent All-Pairs Field Transforms for Optical Flowcited by 2,415
  12. 2020Improved Baselines with Momentum Contrastive Learningcited by 2,287
19 more
  1. 2018Unsupervised Representation Learning by Predicting Image Rotationscited by 1,591
  2. 2019Contrastive Multiview Codingcited by 1,301
  3. 2019A Comprehensive Survey on Transfer Learningcited by 1,160
  4. 2015Unsupervised Visual Representation Learning by Context Predictioncited by 1,066
  5. 2015FlowNet: Learning Optical Flow with Convolutional Networkscited by 1,059
  6. 2019Big Transfer (BiT): General Visual Representation Learningcited by 769
  7. 2018Rethinking ImageNet Pre-trainingcited by 377
  8. 2020Contrastive learning of global and local features for medical image segmentation with limited annotationscited by 309
  9. 2017Toward Multimodal Image-to-Image Translationcited by 296
  10. 2020What Should Not Be Contrastive in Contrastive Learningcited by 226
  11. 2021Seasonal Contrast: Unsupervised Pre-Training from Uncurated Remote Sensing Datacited by 215
  12. 2021Can contrastive learning avoid shortcut solutions?cited by 114
  13. 2021Generative Models as a Data Source for Multiview Representation Learningcited by 104
  14. 2021AutoFlow: Learning a Better Training Set for Optical Flowcited by 102
  15. 2021Pre-training without Natural Imagescited by 100
  16. 2021Learning to See by Looking at Noisecited by 92
  17. 2021Can Vision Transformers Learn without Natural Images?cited by 32
  18. 2021Does Pretraining for Summarization Require Knowledge Transfer?cited by 30
  19. 2021Improving Fractal Pre-trainingcited by 26

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 4; 2024: 12; 2025: 10; 2026: 2.

28 papers.

Sort:

By area:

  1. 2024The Platonic Representation Hypothesiscited by 382
  2. 2023FreeMask: Synthetic Images with Dense Annotations Make Stronger Segmentation Modelscited by 61
  3. 2024A Vision Check-up for Language Modelscited by 36
  4. 2023Differentially Private Image Classification by Learning Priors from Random Processescited by 19
  5. 2025Ambient Diffusion Omni: Training Good Models with Bad Datacited by 18
  6. 2023ViP: A Differentially Private Foundation Model for Computer Visioncited by 17
  7. 2024PLeaS -- Merging Models with Permutations and Least Squarescited by 16
  8. 2026Training Language Models via Neural Cellular Automatacited by 15
  9. 2024Scaling Backwards: Minimal Synthetic Pre-training?cited by 14
  10. 2026Procedural Pretraining: Warming Up Language Models with Abstract Datacited by 10
  11. 2024What Makes a Good Dataset for Knowledge Distillation?cited by 9
  12. 2025Transformers Pretrained on Procedural Data Contain Modular Structures for Algorithmic Reasoningcited by 9
16 more
  1. 2025Can You Learn to See Without Images? Procedural Warm-Up for Vision Transformerscited by 8
  2. 2024Differentially Private Representation Learning via Image Captioningcited by 5
  3. 2025Elastic ViTs from Pretrained Models without Retrainingcited by 4
  4. 2024Pre-training with Synthetic Patterns for Audiocited by 3
  5. 2024Learning Video Representations without Natural Videoscited by 3
  6. 2023Data-Free Knowledge Distillation Using Adversarially Perturbed OpenGL Shader Imagescited by 2
  7. 2024Data Collection-free Masked Video Modelingcited by 2
  8. 2024Contrastive Learning from Synthetic Audio Doppelg"angerscited by 1
  9. 2024Semantic-Free Procedural 3D Shapes Are Surprisingly Good Teacherscited by 1
  10. 2025Stylized Structural Patterns for Improved Neural Network Pre-trainingcited by 1
  11. 20253DFroMLLM: 3D Prototype Generation only from Pretrained Multimodal LLMscited by 1
  12. 2025Self-supervised Synthetic Pretraining for Inference of Stellar Mass Embedded in Dense Gascited by 1
  13. 2025SciTextures: Collecting and Connecting Visual Patterns, Models, and Code Across Science and Artcited by 1
  14. 2024Detect Fake with Fake: Leveraging Synthetic Data-driven Representation for Synthetic Image Detectioncited by 0
  15. 2025Separating Knowledge and Perception with Procedural Datacited by 0
  16. 2025Towards Reliable and Generalizable Differentially Private Machine Learning (Extended Version)cited by 0