Topic-VQ-VAE: Leveraging Latent Codebooks for Flexible Topic-Guided Document Generation
Authors: YoungJoon Yoo, JongWon Choi
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | 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. |