pytorch-proVLAE is an adaptable PyTorch implementation inspired by the ICLR 2020 paper on progressive learning. It enhances the VAE architecture for flexible configurations, allowing you to train on varying image sizes. Explore hierarchical representation learning with dynamic size management for a more robust performance across datasets.
Pytorch-proVLAE is an advanced PyTorch implementation inspired by the paper Progressive Learning and Disentanglement of Hierarchical Representations by Zhiyuan et al., presented at ICLR 2020. This project aims to enhance hierarchical representation learning through progressive training and disentanglement, mirroring the original concept utilizing TensorFlow with increased flexibility and usability.
Features
- Flexible VAE Configuration: Users can easily configure the VAE architecture by specifying key parameters such as
z_dim, the number of ladder layers, and input image size, making it adaptable to various project needs. - Dynamic Size Management: The implementation smartly manages size adjustments for arbitrary input image dimensions, allowing automatic calculations for maximum ladder layers and appropriate dimensional handling for feature maps during processing.
- Visualization of Latent Space: Experience the results of latent space traversal with stunning visualizations when trained on diverse datasets. The images below illustrate the outcomes from various datasets:
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Implementation Details
This repository features enhancements to allow seamless processing and fine-tuning of hyperparameters. The functionality includes:
- Batch Processing: Train models efficiently with configurable batch sizes and learning rates using state-of-the-art optimizer algorithms like Adam and AdamW.
- Progressive Learning: Implement a ladder architecture that promotes progressive training, enabling an evolution of the VAE models throughout the process.
- Advanced Datasets: The repository supports multiple datasets including MNIST, 3D Shapes, and more, with additional datasets in the pipeline for even broader testing and validation.
Work in Progress
Currently, ongoing work focuses on hyperparameter optimization and the establishment of disentanglement metrics, with future updates promising benchmark results and improved functionality to further refine the user experience.
Get Started
Explore the full potential of latent variable learning and model optimization with pytorch-proVLAE. Harness the power of progressive learning in your projects and take advantage of this cutting-edge implementation that breaks new ground in hierarchical representation learning.