Unsupervised learning of disentangled representations is an open problem in machine learning. The Disentanglement-PyTorch library is developed to facilitate research, implementation, and testing of new variational algorithms. In this modular library, neural architectures, dimensionality of the latent space, and the training algorithms are fully decoupled, allowing for independent and consistent experiments across variational methods. The library handles the training scheduling, logging, and visualizations of reconstructions and latent space traversals. It also evaluates the encodings based on various disentanglement metrics. The library, so far, includes implementations of the following unsupervised algorithms VAE, Beta-VAE, Factor-VAE, DIP-I-VAE, DIP-II-VAE, Info-VAE, and Beta-TCVAE, as well as conditional approaches such as CVAE and IFCVAE. The library is compatible with the Disentanglement Challenge of NeurIPS 2019, hosted on AICrowd and was used to compete in the first and second stages of the challenge, where it was ranked among the best few participants. There are two overlapping avenues in representation learning. One focuses on learning task-specific transformations often optimized towards specific domains and applications. The other approach learns the intrinsic factors of variation, in a disentangled and taskinvariant fashion. The unsupervised disentanglement of latent factors, where changes in a single factor of variation shifts the latent encoding in a single direction, is an open problem of representation learning (Bengio et al., 2013; Lake et al., 2016) . Disentangled representations are valuable in few-shot learning, reinforcement learning, transfer learning, as well as semisupervised learning (Bengio et al., 2013; Peters et al., 2017; . In this work, we developed a library based on the functionalities of the PyTorch framework, which facilitates research, implementation, and testing of new variational algorithms focusing on representation learning and disentanglement. The library branches from the Disentanglement Challenge of NeurIPS 2019, hosted on AICrowd (aicrowd.com), and was used to compete in the first and second stages of the challenge where it was highly ranked. The Disentanglement-PyTorch library is released under the GNU General Public License at https://github.com/amir-abdi/disentanglement-pytorch.
Disentanglement-PyTorch is a library for variational representation learning
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