google-deepmind / 3d-shapesLinks
This repository contains the 3D shapes dataset, used in Kim, Hyunjik and Mnih, Andriy. "Disentangling by Factorising." In Proceedings of the 35th International Conference on Machine Learning (ICML). 2018. to assess the disentanglement properties of unsupervised learning methods.
☆148Updated last year
Alternatives and similar repositories for 3d-shapes
Users that are interested in 3d-shapes are comparing it to the libraries listed below
Sorting:
- ☆79Updated 4 months ago
- Quantitative evaluation of disentangled representations☆62Updated 7 years ago
- Multi-object image datasets with ground-truth segmentation masks and generative factors.☆272Updated 3 years ago
- Code for Implicit Generation and Generalization with Energy Based Models☆354Updated 2 years ago
- Implicit Generation and Generalization in Energy Based Models in PyTorch☆66Updated 6 years ago
- Disentanglement library for PyTorch☆281Updated 3 years ago
- An implementation of the MONet model for unsupervised scene decomposition in PyTorch☆59Updated 3 years ago
- Code for the paper "Contrastive Learning Inverts the Data Generating Process".☆90Updated last year
- Official PyTorch implementation of "SPACE: Unsupervised Object-Oriented Scene Representation via Spatial Attention and Decomposition"☆104Updated last year
- PyTorch re-implementation of Multi-Object Representation Learning with Iterative Variational Inference☆59Updated 2 years ago
- Library for the training and evaluation of object-centric models (ICML 2022)☆68Updated 2 years ago
- Burgess et al. "MONet: Unsupervised Scene Decomposition and Representation"☆88Updated 2 years ago
- ☆123Updated 2 years ago
- PyTorch Implementation of Neural Statistician☆60Updated 3 years ago
- Official PyTorch implementation of GENESIS and GENESIS-V2☆110Updated 3 years ago
- Real NVP PyTorch a Minimal Working Example | Normalizing Flow☆141Updated 4 years ago
- EfficientMORL (ICML'21)☆21Updated 3 years ago
- Dataset to assess the disentanglement properties of unsupervised learning methods☆510Updated 4 years ago
- ☆180Updated 6 years ago
- Pytorch implementation of Block Neural Autoregressive Flow☆181Updated 4 years ago
- This repository contains code for applying Riemannian geometry in machine learning.☆77Updated 4 years ago
- ☆237Updated 6 years ago
- Repository for the paper "Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images"☆446Updated 2 years ago
- Code for reproducing Flow ++ experiments☆189Updated 6 years ago
- Official PyTorch BIVA implementation (BIVA: A Very Deep Hierarchy of Latent Variables for Generative Modeling)☆84Updated 2 years ago
- Towards Nonlinear Disentanglement in Natural Data with Temporal Sparse Coding☆73Updated 3 years ago
- Variational Autoencoder with Spatial Broadcast Decoder☆35Updated 6 years ago
- code for "Isolating Sources of Disentanglement in Variational Autoencoders".☆350Updated 2 years ago
- Simple, extendable, easy to understand Glow implementation in PyTorch☆385Updated 3 years ago
- Code for the paper "VAE with a VampPrior", J.M. Tomczak & M. Welling☆230Updated 7 years ago