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 [1].
Multi-Level Variational Autoencoder: Learning Disentangled Representations From Grouped Observations
Authors: Diane Bouchacourt, Ryota Tomioka, Sebastian Nowozin
AAAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experimentally show that our model (i) learns a semantically meaningful disentanglement, (ii) enables control over the latent representation, and (iii) generalises to unseen groups. |
| Researcher Affiliation | Collaboration | Diane Bouchacourt OVAL Group University of Oxford EMAIL Ryota Tomioka, Sebastian Nowozin Machine Intelligence and Perception Group Microsoft Research Cambridge, UK EMAIL |
| Pseudocode | Yes | Algorithm 1: ML-VAE training algorithm. |
| Open Source Code | No | The paper does not contain an explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We perform evaluation on MNIST (Le Cun et al. 1998). ... Next, we perform evaluation on the face aligned version of the MS-Celeb-1M data set (Guo et al. 2016). |
| Dataset Splits | Yes | We randomly separate the 60, 000 training examples into 50, 000 training samples and 10, 000 validation samples, and use the standard MNIST testing data set. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU or CPU models, processor types, or memory amounts used for running experiments. |
| Software Dependencies | No | The paper does not specify particular software dependencies with version numbers (e.g., programming languages or libraries like Python 3.x, PyTorch 1.x). |
| Experiment Setup | Yes | The style and content vectors are of size 10 each. The decoder network is composed a linear layer with 500 hidden units with the hyperbolic tangent activation function. It is followed by two linear layers of 784 hidden units each that output respectively the mean and log-variance of p(xi|c G,i, si; θ). |