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].
Local Disentanglement in Variational Auto-Encoders Using Jacobian $L_1$ Regularization
Authors: Travers Rhodes, Daniel Lee
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We apply our novel JL1-VAE framework to a variety of datasets, giving qualitative and quantitative results showing that our added L1 cost can encourage local alignment of the axes of the latent representation with individual factors of variation. |
| Researcher Affiliation | Academia | Travers Rhodes Department of Computer Science Cornell Tech, Cornell University New York, NY 10044 EMAIL Daniel D. Lee Department of Electrical and Computer Engineering Cornell Tech, Cornell University New York, NY 10044 EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions using `disentanglement_lib` which is open-source, but it does not state that the authors are releasing their own code for the JL1-VAE methodology. |
| Open Datasets | Yes | The first are natural images in grayscale taken by Olshausen and Field [7]... The second is a dataset of simulated 64x64-pixel grayscale images of three black dots on a white background, inspired by [34]. ... Finally, we also apply our approach to tiled images of a real robotic arm taken from the MPI3D-real dataset [35], licensed under Creative Commons Attribution 4.0 International License. |
| Dataset Splits | No | The paper describes training on batches and total batches, but does not specify explicit train/validation/test dataset splits with percentages, counts, or references to predefined splits. |
| Hardware Specification | Yes | We train these models on a Nvidia Quadro V100 hosted locally and one hosted on Google Cloud. ... we train on a Nvidia Quadro V100s hosted locally. |
| Software Dependencies | No | The paper mentions software like Adam optimizer and `disentanglement_lib` but does not provide specific version numbers for these or other key software components, which is required for reproducibility. |
| Experiment Setup | Yes | We use the Adam optimizer with a learning rate of 0.0001 (matching [11]). We use linear annealing from 0 to the final hyperparameter value over the first 100,000 batches for both the beta hyperparameter and JL1-VAE s γ parameter in our implementations for JL1-VAE and β-VAE (unlike [11]). ... We use a latent dimension of ten for all experiments |