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..
Multi-Facet Clustering Variational Autoencoders
Authors: Fabian Falck, Haoting Zhang, Matthew Willetts, George Nicholson, Christopher Yau, Chris C Holmes
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | On image benchmarks, we demonstrate that our approach separates out and clusters over different aspects of the data in a disentangled manner. We demonstrate the usefulness of our model and its prior structure in four experimental analyses: (a) discovering a multi-facet structure (b) compositionality of latent facets (c) generative, unsupervised classification, and (d) diversity of generated samples from our model. We train our model on three image datasets: MNIST [5], 3DShapes (two configurations) [24] and SVHN [25]. |
| Researcher Affiliation | Academia | 1University of Oxford 2University of Cambridge 3University College London 4University of Manchester 5Health Data Research UK 6The Alan Turing Institute |
| Pseudocode | No | No pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | Yes | We also provide our code implementing MFCVAE, using Py Torch Distributions [26], and reproducing our results at https://github.com/Fabian Falck/mfcvae. |
| Open Datasets | Yes | We train our model on three image datasets: MNIST [5], 3DShapes (two configurations) [24] and SVHN [25]. |
| Dataset Splits | No | For all datasets, we use the standard training and test splits. |
| Hardware Specification | No | All computational experiments were carried out on a cluster infrastructure. No specific hardware details (e.g., GPU/CPU models, memory) were provided. |
| Software Dependencies | No | The paper lists software packages like PyTorch, NumPy, Matplotlib, Seaborn, OpenCV, and Scikit-learn but does not provide specific version numbers for any of them. |
| Experiment Setup | Yes | We run each model for 500 epochs with Adam [60] with a learning rate of 1e-4 and a batch size of 128. We found this set of hyperparameters to work well across all datasets and settings for the number of facets and for each facet s number of components... We use a learning rate of 1e-4, and we warm-up the learning rate to 1e-4 over 1000 steps, and then linearly decay the learning rate to 0 over 500000 steps. We use a batch size of 128. |