Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
MMVAE+: Enhancing the Generative Quality of Multimodal VAEs without Compromises
Authors: Emanuele Palumbo, Imant Daunhawer, Julia E Vogt
ICLR 2023 | Venue PDF | 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. |