Omkar2118/datasets huggingface dataset:
Natural Sprites

This csv consists of (x-position, y-position, area) tuples of three views (left, middle, right) of downscaled binary masks with aspect ratio kept (64 x 128) from the 2019 YouTube-VIS challenge, which can be found at https://competitions.codalab.org/competitions/20127#participate-get-data. Extracting pairs from this csv results in 234,652 transitions in the given statistics. These statistics can be used to augment ground truth factor distributions with natural transitions, which we demonstrate with spriteworld. For details, we refer to our paper, which can be found at https://openreview.net/forum?id=EbIDjBynYJ8.

Introducing paper: Towards Nonlinear Disentanglement in Natural Data with Temporal Sparse Coding
KITTI-Masks

This Dataset consists of 2120 sequences of binary masks of pedestrians. The sequence length varies between 2-710. For details, we refer to our paper. It is based on the original KITTI Segmentation challenge which can be found at https://www.vision.rwth-aachen.de/page/mots 

A detailed description can be found at: https://openreview.net/pdf?id=EbIDjBynYJ8

An example dataloader can be found at: 
https://github.com/bethgelab/slow_disentanglement/

Introducing paper: Towards Nonlinear Disentanglement in Natural Data with Temporal Sparse Coding
3DIdent

Novel benchmark which features aspects of natural scenes, e.g. a complex 3D object and different lighting conditions, while still providing access to the continuous ground-truth factors.

We use the Blender rendering engine to create visually complex 3D images. Each image in the dataset shows a colored 3D object which is located and rotated above a colored ground in a 3D space. Additionally, each scene contains a colored spotlight which is focused on the object and located on a half-circle around the scene. The observations are encoded with an RGB color space, and the spatial resolution is 224x224 pixels.

The images are rendered based on a 10-dimensional latent, where: (1) three dimensions describe the XYZ position, (2) three dimensions describe the rotation of the object in Euler angles, (3) two dimensions describe the color of the object and the ground of the scene, respectively, and (4) two dimensions describe the position and color of the spotlight. We use the HSV color space to describe the color of the object and the ground with only one latent each by having the latent factor control the hue value.

The training set and test set contain 250,000 and 25,000 observation-latent pairs, respectively, whereby the latents are uniformly sampled from the unit hyperrectangle.

Introducing paper: Contrastive Learning Inverts the Data Generating Process
Causal3DIdent

Update on 3DIdent, where we introduce six additional object classes (Hare, Dragon, Cow, Armadillo, Horse, and Head), and impose a causal graph over the latent variables. For further details, see Appendix B in the associated paper (https://arxiv.org/abs/2106.04619).

Introducing paper: Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style