Learning Disentangled Joint Continuous and Discrete Representations
Authors: Emilien Dupont
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show that the framework disentangles continuous and discrete generative factors on various datasets and outperforms current disentangling methods when a discrete generative factor is prominent. |
| Researcher Affiliation | Industry | Emilien Dupont Schlumberger Software Technology Innovation Center Menlo Park, CA, USA dupont@slb.com |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. Figure 1 shows an architecture diagram, not pseudocode. |
| Open Source Code | Yes | The code, along with all experiments and trained models presented in this paper, is available at https://github.com/ Schlumberger/joint-vae. |
| Open Datasets | Yes | We perform experiments on several datasets including MNIST, Fashion MNIST (Xiao et al. (2017)), Celeb A (Liu et al. (2015)) and Chairs (Aubry et al. (2014)). We quantitatively evaluate our model on the d Sprites dataset using the metric recently proposed by Kim & Mnih (2018). |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, or detailed splitting methodology) for training, validation, or testing. |
| 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 does not provide specific ancillary software details with version numbers (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | The model was trained with 10 continuous latent variables and one discrete 10-dimensional latent variable [for MNIST]. For Celeb A we used a model with 32 continuous latent variables and one 10 dimensional discrete latent variable. For the chairs dataset we used a model with 32 continuous latent variables and 3 binary discrete latent variables. The Joint VAE loss in equation 7 depends on the hyperparameters γ, Cc and Cz. All hyperparameters needed to reproduce the results in this paper are given in the appendix. |