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..
Unsupervised Stylish Image Description Generation via Domain Layer Norm
Authors: Cheng-Kuan Chen, Zhufeng Pan, Ming-Yu Liu, Min Sun8151-8158
AAAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental validation and user study on various stylish image description generation tasks are conducted to show the competitive advantages of the proposed model. |
| Researcher Affiliation | Collaboration | 1Department of Electrical Engineering, National Tsing Hua University 2NVIDIA |
| Pseudocode | No | The paper describes the model architecture and training process using diagrams and mathematical equations, but it does not include explicit pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions 'The implementation details are in the supplementary' but does not explicitly state that source code is provided or offer a link to a repository. |
| Open Datasets | Yes | We use paragraphs released in (Krause et al. 2017) (VG-Para) as our source domain dataset. ... We use humor and romance novel collections in Book Corpus (Zhu et al. 2015). |
| Dataset Splits | Yes | We use pre-split data which contain 14575, 2489 and 2487 for training, validation and testing. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU/CPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper describes the use of CNNs, RNNs, Skip-Thought Vectors, and LN-LSTM, but does not provide specific version numbers for any software dependencies like programming languages, libraries, or frameworks. |
| Experiment Setup | No | The paper states 'The implementation details are in the supplementary,' but the main text does not include specific hyperparameters (e.g., learning rate, batch size) or other detailed experimental setup configurations. |