mlsanityData to 7 Oct 2026

Exploring Invariance in Images through One-way Wave Equations

arXiv 2310.12976 ↗First posted 19 Oct 2023cs.CV cs.LGcited by 1

Not on the map. Why?

Abstract (arXiv)

In this paper, we empirically reveal an invariance over images-images share a set of one-way wave equations with latent speeds. Each image is uniquely associated with a solution to these wave equations, allowing for its reconstruction with high fidelity from an initial condition. We demonstrate it using an intuitive encoder-decoder framework where each image is encoded into its corresponding initial condition (a single vector). Subsequently, the initial condition undergoes a specialized decoder, transforming the one-way wave equations into a first-order norm + linear autoregressive process. This process propagates the initial condition along the x and y directions, generating a high-resolution feature map (up to the image resolution), followed by a few convolutional layers to reconstruct image pixels. The revealed invariance, rooted in the shared wave equations, offers a fresh perspective for comprehending images, establishing a promising avenue for further exploration.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 48 of its 64 references that are in the map. First posted on arXiv 19 Oct 2023.

Builds on

48 of 64 references.

References · 48

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2020Language Models are Few-Shot Learnerscited by 32,199
  4. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  5. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  6. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  7. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 11,979
  8. 2018Representation Learning with Contrastive Predictive Codingcited by 7,662
  9. 2019Momentum Contrast for Unsupervised Visual Representation Learningcited by 7,482
  10. 2020End-to-End Object Detection with Transformerscited by 7,384
  11. 2021Masked Autoencoders Are Scalable Vision Learnerscited by 7,339
  12. 2021Emerging Properties in Self-Supervised Vision Transformerscited by 6,893
36 more
  1. 2018MobileNetV2: Inverted Residuals and Linear Bottleneckscited by 4,773
  2. 2020Bootstrap your own latent: A new approach to self-supervised Learningcited by 4,712
  3. 2017Neural Discrete Representation Learningcited by 4,650
  4. 2021Zero-Shot Text-to-Image Generationcited by 4,276
  5. 2020Deformable DETR: Deformable Transformers for End-to-End Object Detectioncited by 3,410
  6. 2020Taming Transformers for High-Resolution Image Synthesiscited by 3,229
  7. 2020Exploring Simple Siamese Representation Learningcited by 2,853
  8. 2021BEiT: BERT Pre-Training of Image Transformerscited by 2,360
  9. 2020Improved Baselines with Momentum Contrastive Learningcited by 2,282
  10. 2018Unsupervised Feature Learning via Non-Parametric Instance-level Discriminationcited by 1,838
  11. 2021An Empirical Study of Training Self-Supervised Vision Transformerscited by 1,610
  12. 2019Generating Diverse High-Fidelity Images with VQ-VAE-2cited by 1,376
  13. 2020Big Self-Supervised Models are Strong Semi-Supervised Learnerscited by 1,375
  14. 2022Scaling Autoregressive Models for Content-Rich Text-to-Image Generationcited by 1,287
  15. 2022CoCa: Contrastive Captioners are Image-Text Foundation Modelscited by 1,265
  16. 2021SimMIM: A Simple Framework for Masked Image Modelingcited by 1,147
  17. 2021iBOT: Image BERT Pre-Training with Online Tokenizercited by 891
  18. 2016Conditional Image Generation with PixelCNN Decoderscited by 859
  19. 2016Pixel Recurrent Neural Networkscited by 814
  20. 2021Masked Feature Prediction for Self-Supervised Visual Pre-Trainingcited by 573
  21. 2022DAB-DETR: Dynamic Anchor Boxes are Better Queries for DETRcited by 569
  22. 2022DN-DETR: Accelerate DETR Training by Introducing Query DeNoisingcited by 486
  23. 2022Masked Siamese Networks for Label-Efficient Learningcited by 301
  24. 2021Mobile-Former: Bridging MobileNet and Transformercited by 234
  25. 2021PeCo: Perceptual Codebook for BERT Pre-training of Vision Transformerscited by 204
  26. 2017PixelSNAIL: An Improved Autoregressive Generative Modelcited by 137
  27. 2022Contrastive Masked Autoencoders are Stronger Vision Learnerscited by 133
  28. 2022ConvMAE: Masked Convolution Meets Masked Autoencoderscited by 105
  29. 2022Siamese Image Modeling for Self-Supervised Vision Representation Learningcited by 78
  30. 2022Corrupted Image Modeling for Self-Supervised Visual Pre-Trainingcited by 68
  31. 2022Bootstrapped Masked Autoencoders for Vision BERT Pretrainingcited by 60
  32. 2022Uniform Masking: Enabling MAE Pre-training for Pyramid-based Vision Transformers with Localitycited by 57
  33. 2022A Closer Look at Self-Supervised Lightweight Vision Transformerscited by 31
  34. 2021MicroNet: Improving Image Recognition with Extremely Low FLOPscited by 26
  35. 2022Masked Siamese ConvNetscited by 22
  36. 2021Improve Unsupervised Pretraining for Few-label Transfercited by 15

Built on by

Cited by year

Citing papers per year: 2024: 1.

1 paper.

  1. 2024On a Hidden Property in Computational Imagingcited by 0