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..
Topic-VQ-VAE: Leveraging Latent Codebooks for Flexible Topic-Guided Document Generation
Authors: YoungJoon Yoo, JongWon Choi
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental results on document analysis and image generation demonstrate that TVQ-VAE effectively captures the topic context which reveals the underlying structures of the dataset and supports flexible forms of document generation." and "Empirical Analysis We analyze the TVQ-VAE performance with two different applications: document analysis and image generation. |
| Researcher Affiliation | Collaboration | Young Joon Yoo1, Jongwon Choi2 1 Image Vision, NAVER Cloud. 2 Department of Advanced Imaging (GSAIM) and Graduate School of AI, Chung-Ang University. |
| Pseudocode | Yes | Algorithm 1: Pseudo-code of TVQ-VAE generation" and "Algorithm 2: Pseudo-code of TVQ-VAE training |
| Open Source Code | Yes | Official implementation of the proposed TVQ-VAE is available at https: //github.com/clovaai/TVQ-VAE. |
| Open Datasets | Yes | We conduct experiments on two datasets: 20 Newsgroups (20NG) (Lang 1995), the New York Timesannotated corpus (NYT) (Sandhaus 2008)... two image datasets: CIFAR10 (Krizhevsky, Hinton et al. 2009) and Celeb A (Liu et al. 2015) |
| Dataset Splits | No | The paper mentions 'test set' in the context of evaluation but does not specify explicit training, validation, and test dataset splits with percentages or counts. |
| Hardware Specification | No | The paper does not specify any hardware details such as GPU/CPU models, processor types, or memory used for running the experiments. |
| Software Dependencies | No | The paper mentions tools like 'sentence Bert' and 'Word2Vec' but does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | For TVQ-VAE, we set the embedding number and expansion k to 300 and 5. |