PROGRESSIVE LEARNING AND DISENTANGLEMENT OF HIERARCHICAL REPRESENTATIONS
Authors: Zhiyuan Li, Jaideep Vitthal Murkute, Prashnna Kumar Gyawali, Linwei Wang
ICLR 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We quantitatively demonstrate the ability of the presented model to improve disentanglement in comparison to existing works on two benchmark data sets using three disentanglement metrics, including a new metric we proposed to complement the previously-presented metric of mutual information gap. |
| Researcher Affiliation | Academia | Zhiyuan Li, Jaideep Vitthal Murkute, Prashnna Kumar Gyawali & Linwei Wang Golisano College of Computing and Information Sciences Rochester Institute of Technology Rochester, NY 14623, USA {zl7904,jvm6526,pkg2182,Linwei.Wang}@rit.edu |
| Pseudocode | No | The paper describes the model and methods in prose and mathematical equations but does not include any distinct pseudocode or algorithm blocks. |
| Open Source Code | Yes | Source code available at https://github.com/Zhiyuan1991/proVLAE. |
| Open Datasets | Yes | We tested the presented pro-VLAE on four benchmark data sets: d Sprites (Matthey et al. (2017)), 3DShapes (Burgess & Kim (2018)), MNIST (Le Cun et al. (1998)), and Celeb A (Liu et al. (2015)), where the first two include ground-truth generative factors that allow us to carry out comprehensive quantitative comparisons of disentangling metrics with existing models. |
| Dataset Splits | No | The paper mentions training epochs and datasets, but does not provide specific train/validation/test split percentages, sample counts, or explicit splitting methodology needed to reproduce the data partitioning. It uses terms like 'train' in the context of the model, but not for dataset splits explicitly. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, processor types, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | In specific, we introduce a fade-in coefficient α to equations (6) and (7) when growing new components in the encoder and the decoder: [...] where α increases from 0 to 1 within a certain number of iterations (5000 in our experiments) since the addition of the new network components µl,σl, and ml. [...] where γ is set to 0.5 in our experiments. |