Published May 24, 2022
| Version v5
Dataset
Open
Training and test data, plus saved models for the paper "Top-down inference in an early visual cortex inspired hierarchical Variational Autoencoder" submitted to NeurIPS 2022
Description
Each file is in the pickle format and was generated with Python 3.8.5.
Each file contains a Python dictionary with the following fields:
- 'train_images': 640,000 float32 images used for model training. 20px images contain 400 pixel intensities, 40px images contain 1600 pixel intensities each.
- 'train_labels': float32 labels for each image in 'train_images'. All natural images are labeled with 0.0. Texture images are labeled with 0.0, 1,0, 2.0, 3.0, or 4.0, according to their texture family.
- 'test_images': 64,000 float32 images used for model testing. 20px images contain 400 pixel intensities, 40px images contain 1600 pixel intensities each.
- 'test_labels': float32 labels for each image in 'test_images'. All natural images are labeled with 0.0. Texture images are labeled with 0.0, 1,0, 2.0, 3.0, or 4.0, according to their texture family.
For more details, see the paper "Top-down inference in an early visual cortex inspired hierarchical Variational Autoencoder" submitted to NeurIPS 2022 (to be published soon on arXiv.org).
Details on saved models coming soon.
Files
chain_20.zip
Files
(13.7 GB)
| Name | Size | Download all |
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md5:9d342855a841200ff3d815d9cb7045ed
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23.2 MB | Preview Download |
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md5:abaa97ab4df7c684306566393b8c0c32
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22.6 MB | Preview Download |
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md5:c3792799a7073d3cb8bfdeadde0813a2
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302.2 MB | Preview Download |
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md5:378bebd06b105998c05aa73752de77c7
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191.0 MB | Preview Download |
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md5:db3b69f2a1d86c5d4047c6d661fbabd3
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1.9 GB | Preview Download |
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md5:99cdcefdde1dfdc434fb3dc538adcb16
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1.1 GB | Download |
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md5:7103d0a8dc4f4c4ca2ac85d0468a3066
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4.5 GB | Download |
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md5:4c1ccbff7f5f0f09f987f74c197499ef
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1.1 GB | Download |
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md5:f3c08590226da6789babf073329ea2bc
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4.5 GB | Download |
Additional details
References
- J Hans Van Hateren and Arjen van der Schaaf. Independent component filters of natural images compared445 with simple cells in primary visual cortex. Proceedings of the Royal Society of London. Series B: Biological446 Sciences, 265(1394):359–366, 1998.
- Javier Portilla and Eero P Simoncelli. A parametric texture model based on joint statistics of complex430 wavelet coefficients. International journal of computer vision, 40(1):49–70, 2000.
- https://www.textures.com/
- Phil Brodatz. Textures: a photographic album for artists and designers. Dover publications, 1966.
- Gabriel Barello, Adam S Charles, and Jonathan W Pillow. Sparse-coding variational auto-encoders.355 bioRxiv, page 399246, 2018.