Variational Mixture-of-Experts Autoencoders for Multi-Modal Deep Generative Models
Authors: Yuge Shi, Siddharth N, Brooks Paige, Philip Torr
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To evaluate our model, we constructed two multi-modal scenarios to conduct experiments on. The first experiment involves many-to-many image image transforms on matching digits between the MNIST and street-view house numbers (SVHN) datasets. ... For each of these experiments, we provide both qualitative and quantitative analyses of the extent to which our model satisfies the four proposed criteria... |
| Researcher Affiliation | Academia | Yuge Shi N. Siddharth Department of Engineering Science University of Oxford {yshi, nsid}@robots.ox.ac.uk Brooks Paige Alan Turing Institute & University of Cambridge bpaige@turing.ac.uk Philip H.S. Torr Department of Engineering Science University of Oxford philip.torr@eng.ox.ac.uk |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code, data, and models are provided at this url. ... Source code for all models and experiments is available at https://github.com/iffsid/mmvae. |
| Open Datasets | Yes | Code, data, and models are provided at this url. ... Data and pre-trained models from our experiments are also available at https://github.com/iffsid/mmvae. ... We employ the images and captions from Caltech-UCSD Birds (CUB) dataset (Wah et al., 2011)... |
| Dataset Splits | No | The paper does not provide specific details on training, validation, and test dataset splits (e.g., exact percentages or sample counts) for reproducibility, beyond mentioning a 'training set' and 'test set' in general terms. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for experiments (e.g., GPU models, CPU models, or memory specifications). |
| Software Dependencies | No | The paper mentions software components like 'Adam optimiser' and 'AMSGrad', and models like 'Res Net-101' and 'Fast Text', but does not specify any version numbers for these or other software dependencies. |
| Experiment Setup | Yes | For learning, we use the Adam optimiser (Kingma and Ba, 2014) with AMSGrad (Reddi et al., 2018), with a learning rate of 0.001. ... Here, we use CNNs for SVHN and MLPs for MNIST, with a 20d latent space. ... We use 128-dimensional latents with a Laplace likelihood on image features and a Categorical likelihood for captions. |