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..
IRC-GAN: Introspective Recurrent Convolutional GAN for Text-to-video Generation
Authors: Kangle Deng, Tianyi Fei, Xin Huang, Yuxin Peng
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct experiments on 3 datasets and compare with state-of-the-art methods. |
| Researcher Affiliation | Academia | Kangle Deng , Tianyi Fei , Xin Huang and Yuxin Peng Institute of Computer Science and Technology, Peking University, Beijing, China EMAIL |
| Pseudocode | No | The paper does not contain any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not mention releasing source code or provide any links to a code repository. |
| Open Datasets | No | The paper states, 'Since [Mittal et al., 2016] didn t make open their dataset or source codes to construct it, we construct our own KTH-4 according to the method mentioned in [Mittal et al., 2016].' While they use existing datasets as a basis, there is no explicit statement or link confirming the public availability of their *modified* datasets used in the experiments. |
| Dataset Splits | No | The paper mentions using '3 datasets' but does not specify the training, validation, or test splits (e.g., percentages or sample counts) used for these datasets. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., GPU model, CPU type, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not list any specific software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.x, TensorFlow 2.x). |
| Experiment Setup | Yes | So it is natural to perform a two-stage training process: ๏ฌrst pre-training the text encoder and then training the whole model with the encoder ๏ฌxed. |