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Cited by 48Image and Video Quality AssessmentDigital Media Forensic DetectionImage Retrieval and Classification Techniques

The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.

Shift-Tolerant Perceptual Similarity Metric

Open access
Ghildyal, Abhijay, Liu, Feng·2022·Lecture Notes in Computer Science
DOI 10.1007/978-3-031-19797-0_6
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Research works printed but not linked: 17

  1. Athalye, A., Carlini, N., Wagner, D.: Obfuscated gradients give a false sense of security: circumventing defenses to adversarial examples. In: International Conference on Machine Learning. vol. 80, pp. 274–283 (2018)
  2. Azulay, A., Weiss, Y.: Why do deep convolutional networks generalize so poorly to small image transformations? J. Mach. Learn. Res. 20(184), 1–25 (2019)
  3. Bhardwaj, S., Fischer, I., Ballé, J., Chinen, T.: An unsupervised information-theoretic perceptual quality metric. In: Advances in Neural Information Processing Systems, pp. 13–24 (2020)
  4. CLIC: Workshop and challenge on learned image compression (2021). http://www.compression.cc/2021/
  5. Czolbe, S., Krause, O., Cox, I., Igel, C.: A loss function for generative neural networks based on Watson’s perceptual model. In: Advances in Neural Information Processing Systems, pp. 2051–2061 (2020)
  6. Dosovitskiy, A., Brox, T.: Generating images with perceptual similarity metrics based on deep networks. In: Advances in Neural Information Processing Systems, pp. 658–666 (2016)
  7. Hénaff, O.J., Simoncelli, E.P.: Geodesics of learned representations. In: International Conference on Learning Representations (2016)
  8. Iandola, F.N., Han, S., Moskewicz, M.W., Ashraf, K., Dally, W.J., Keutzer, K.: Squeezenet: Alexnet-level accuracy with 50x fewer parameters and $$<$$0.5mb model size. arXiv/1602.07360 (2016)
  9. Islam, M.A., Jia, S., Bruce, N.D.B.: How much position information do convolutional neural networks encode? In: International Conference on Learning Representations (2020)
  10. Kettunen, M., Härkönen, E., Lehtinen, J.: E-LPIPS: robust perceptual image similarity via random transformation ensembles. arXiv/1906.03973 (2019)
  11. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1106–1114 (2012)
  12. Nair, V., Hinton, G.E.: Rectified linear units improve restricted boltzmann machines. In: International Conference on Machine Learning, pp. 807–814 (2010)
  13. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: International Conference on Learning Representations (2015)
  14. Wang, Z., Simoncelli, E.P., Bovik, A.C.: Multiscale structural similarity for image quality assessment. In: Asilomar Conference on Signals, Systems & Computers, vol. 2, pp. 1398–1402. IEEE (2003)
  15. Xiao, C., Zhu, J.Y., Li, B., He, W., Liu, M., Song, D.: Spatially transformed adversarial examples. In: International Conference on Learning Representations (2018)
  16. Zhang, R.: Making convolutional networks shift-invariant again. In: International Conference on Machine Learning, pp. 7324–7334 (2019)
  17. Zou, X., Xiao, F., Yu, Z., Lee, Y.J.: Delving deeper into anti-aliasing in convnets. In: British Machine Vision Conference (2020)

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Metadata

Authors
Ghildyal, A., & Liu, F.
Affiliations
Portland State University (US)
Corresponding author
Ghildyal, Abhijay
ORCID
Venue
Lecture Notes in Computer Science
Publisher
Springer Nature Switzerland
Published
2022
Pages
91-107
ISSN
  • 0302-9743 (ISSN-L)
  • 1611-3349
DOI
10.1007/978-3-031-19797-0_6
OpenAlex
W4312968447
Cited by
47
References
22
Citations by year
2026: 14 · 2025: 19 · 2024: 11 · 2023: 3
FWCI
2.99
Type
conference paper
Language
en
Open access
Licence
Locations
  • Lecture notes in computer science (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Video Quality Assessment
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
Keywords
image misalignment, LPIPS, anti-aliasing filtering, pooling, padding, skip connections, human similarity judgments

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