Published May 24, 2022
| Version v9
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 .pkl file contains a training or test dataset in the form of a Python dictionary (generated with Python 3.8.5) 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.
Each .zip file contains a saved model. Details on these are coming soon.
For more details, see the paper "Top-down inference in an early visual cortex inspired hierarchical Variational Autoencoder" submitted to NeurIPS 2022 (preprint).
Files
chain_20.zip
Files
(19.2 GB)
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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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md5:90c61ea6e19c9c71b2c734b19b902130
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1.9 GB | Preview Download |
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md5:06e7299b41785f72ec9ad4c840f70e27
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1.8 GB | Preview Download |
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md5:5df543463207ba298a02680eb1cef5b9
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1.8 GB | Preview Download |
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md5:c261d88d1c3a65958657acfac6be7649
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1.8 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.