Learning Latent Subspaces in Variational Autoencoders
Authors: Jack Klys, Jake Snell, Richard Zemel
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the utility of the learned representations for attribute manipulation tasks on both the Toronto Face [23] and Celeb A [15] datasets. |
| Researcher Affiliation | Academia | Jack Klys, Jake Snell, Richard Zemel University of Toronto Vector Institute {jackklys,jsnell,zemel}@cs.toronto.edu |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., a specific repository link, an explicit statement of code release, or mention of code in supplementary materials) for the methodology described. |
| Open Datasets | Yes | The Toronto Faces Dataset [23] consists of approximately 120,000 grayscale face images partially labelled with expressions... Celeb A [15] is a dataset of approximately 200,000 images of celebrity faces with 40 labelled attributes. |
| Dataset Splits | Yes | This data was randomly split into a train, validation, and test set in 80%/10%/10% proportions (preserving the proportions of originally labelled data in each split). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions 'Scikit-learn [19]' and 'Pytorch [18]' but does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | In practice we use Gaussian MLPs to represent distributions over relevant random variables... we choose Wi = R2 for all i. Hence we let µ1 = (0, 0), σ1 = (0.1, 0.1) and µ2 = (3, 3), σ2 = (1, 1). ... For Cond VAE and Cond VAE-info the points are chosen uniformly in the range [0, 3]. ... we follow the standard practice used in the literature, of setting pj = 1 for the models Cond VAE and Cond VAE-info and set pj to the empirical mean ESj h µj φ2 (x) i over the validation set for CSVAE in analogy with the other models. |