Diffusion Variational Autoencoders
Authors: Luis A. Perez Rey, Vlado Menkovski, Jim Portegies
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show that the VAE is indeed capable of capturing topological properties for datasets with a known underlying latent structure derived from generative processes such as rotations and translations. and 4 Experiments We have implemented 1 VAEs with latent spaces of ddimensional spheres, a flat two-dimensional torus, a torus embedded in R3, the SO(3) and real projective spaces RPd . For all our experiments we used multi-layer perceptrons for the encoder and decoder with three and two hidden layers respectively. |
| Researcher Affiliation | Academia | Luis A. Perez Rey , Vlado Menkovski and Jim Portegies Eindhoven University of Technology, Eindhoven, The Netherlands |
| Pseudocode | No | No explicit pseudocode or algorithm blocks were found. |
| Open Source Code | Yes | 1https://github.com/luis-armando-perez-rey/diffusion vae |
| Open Datasets | Yes | Mainly as a first test of our algorithm, we trained VAEs on binarized MNIST [Salakhutdinov and Murray, 2008]. |
| Dataset Splits | No | The paper mentions training and test datasets but does not explicitly detail training/validation/test splits or specific percentages for validation. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) were provided for the experiments. |
| Software Dependencies | No | The paper does not specify software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | For all our experiments we used multi-layer perceptrons for the encoder and decoder with three and two hidden layers respectively. and We have set S = 10 throughout the presented results. and use an output layer with a tanh activation function to obtain 10 7 t 10 5. |