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..
Visual Concepts Tokenization
Authors: Tao Yang, Yuwang Wang, Yan Lu, Nanning Zheng
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on several popular datasets verify the effectiveness of VCT on the tasks of disentangled representation learning and scene decomposition. VCT achieves the state of the art results by a large margin. |
| Researcher Affiliation | Collaboration | Tao Yang1 , Yuwang Wang2 , Yan Lu2 , Nanning Zheng1 EMAIL, EMAIL, EMAIL 1Xi an Jiaotong University, 2Microsoft Research Asia |
| Pseudocode | No | The paper describes the architecture and processes using figures and textual descriptions, but it does not include any explicit pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | https://github.com/thomasmry/VCT |
| Open Datasets | Yes | Datasets Following [36], we conduct the experiments on the public datasets below, which are popular in disentangled representation literature: Shapes3D [27] is a dataset of 3D shapes generated from 6 factors of variation. MPI3D [17] is a 3D dataset recorded in a controlled environment, defined by 7 factors of variation, and Cars3D [34] is a dataset of CAD models generated by color renderings from 3 factors of variation. |
| Dataset Splits | Yes | Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [Yes] Please see Appendix A |
| Hardware Specification | Yes | Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [Yes] Please see Appendix A |
| Software Dependencies | No | The paper does not provide specific software names with version numbers (e.g., 'PyTorch 1.9', 'Python 3.8') for ancillary software or dependencies. |
| Experiment Setup | Yes | We set Ξ»dis = 1 and adopt VQ-VAE for Lrec in all the experiments. |