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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Towards Building A Group-based Unsupervised Representation Disentanglement Framework
Authors: Tao Yang, Xuanchi Ren, Yuwang Wang, Wenjun Zeng, Nanning Zheng
ICLR 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimentally, we train 1800 models covering the most prominent VAE-based methods on five datasets to verify the effectiveness of our theoretical framework. Compared to the original VAE-based methods, these Groupified VAEs consistently achieve better mean performance with smaller variances. |
| Researcher Affiliation | Collaboration | Yang Tao1 , Xuanchi Ren2 , Yuwang Wang3 , Wenjun Zeng4 , Nanning Zheng1 1Xiโan Jiaotong University, 2HKUST, , 3Microsoft Research Asia, 4EIT |
| Pseudocode | No | No pseudocode or algorithm block was explicitly labeled or formatted as such. |
| Open Source Code | No | The paper references official implementations of *other* methods (e.g., Control VAE and RGr VAE) with GitHub links, but does not state that its *own* proposed method's source code is publicly available. |
| Open Datasets | Yes | To evaluate our method, we consider several datasets: d Sprites (Higgins et al., 2017), Shapes3D (Kim & Mnih, 2018), Cars3D (Reed et al., 2015), and the variants of d Sprites introduced by Locatello et al. (Locatello et al., 2019b): Color-d Sprites and Noisy-d Sprites. |
| Dataset Splits | No | The paper states that 'In all the experiments, we resize the images to 64x64 resolution' and lists datasets, but does not provide specific train/validation/test split percentages or counts. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU models, CPU types, or cloud instance specifications used for running experiments. |
| Software Dependencies | No | The paper mentions 'implemented by Pytorch Paszke et al. (2017)' but does not specify a version number for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | We run using different hyperparameters and random seeds for every VAE-based model implemented by Pytorch Paszke et al. (2017). As shown in Table 4, for ฮฒ-VAE, we assign 3 choices for ฮฒ and 10 random seeds for both the Original and Groupified VAEs: 3x10x2 = 60 settings for each dataset. Similarly, we also assign 60 settings for Factor VAE and ฮฒ-TCVAE. For Anneal VAE, we assign three choices for C and 3 choices for the start and end pair, also assign 10 random seeds. In summary, for all 5 datasets, we run (((3x10x2)x3) + 3x3x10x2)x5 = 1800 models. For other hyperparameters, please refer to Table 5 (b). |