MMVAE+: Enhancing the Generative Quality of Multimodal VAEs without Compromises
Authors: Emanuele Palumbo, Imant Daunhawer, Julia E Vogt
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We report experimental results for two challenging datasets introduced in previous work, namely Poly MNIST (Sutter et al., 2021) and Caltech Birds (CUB) Image-Captions (Shi et al., 2019; Wah et al., 2011). |
| Researcher Affiliation | Academia | 1 ETH AI Center, Z urich, Switzerland 2 Department of Computer Science, ETH Z urich, Switzerland |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | Yes | We provide details about datasets, metrics, hyperparameters and architectures in the Appendix, which can aid reproducibility of our method along with the provided code. |
| Open Datasets | Yes | We report experimental results for two challenging datasets introduced in previous work, namely Poly MNIST (Sutter et al., 2021) and Caltech Birds (CUB) Image-Captions (Shi et al., 2019; Wah et al., 2011). |
| Dataset Splits | No | The paper mentions using a 'test set' for evaluation but does not specify the train/validation/test splits, percentages, or methodology for creating these splits. |
| Hardware Specification | No | The paper does not specify the hardware used for running the experiments. |
| Software Dependencies | Yes | To compute FID scores we use the implementation from Seitzer (2020). |
| Experiment Setup | Yes | We compare all models for a range of representative values of the regularization hyperparameter β {1.0, 2.5, 5.0}. ... All models are trained using the Adam optimizer (Kingma & Ba, 2014). We choose a learning rate of 5e-4 for MVAE and Mo Po E-VAE, following Daunhawer et al. (2022), while for MMVAE and MMVAE+ we choose 1e-3. |