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

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.