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..
ExprGAN: Facial Expression Editing With Controllable Expression Intensity
Authors: Hui Ding, Kumar Sricharan, Rama Chellappa
AAAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Quantitative and qualitative evaluations on the widely used Oulu-CASIA dataset demonstrate the effectiveness of Expr GAN. |
| Researcher Affiliation | Collaboration | Hui Ding,1 Kumar Sricharan,2 Rama Chellappa3 1,3University of Maryland, College Park 2PARC, Palo Alto |
| Pseudocode | No | The paper describes algorithms and training steps in paragraph form, but does not include any clearly labeled "Pseudocode" or "Algorithm" blocks or figures. |
| Open Source Code | No | The paper does not explicitly state that source code for the described methodology is publicly available, nor does it provide a link to a code repository. |
| Open Datasets | Yes | We evaluated the proposed Expr GAN on the widely used Oulu-CASIA (Zhao et al. 2011) dataset. |
| Dataset Splits | Yes | Training and testing sets are divided based on identity, with 1296 for training and 144 for testing. |
| Hardware Specification | No | The paper does not specify the hardware used for training or experimentation, such as specific GPU or CPU models. It only mentions the use of Tensorflow. |
| Software Dependencies | No | The paper mentions "Tensorflow (Abadi et al. 2016)" but does not specify its version number or any other software dependencies with version numbers, such as programming languages or libraries. |
| Experiment Setup | Yes | We train the networks using the Adam optimizer (Kingma and Ba 2014), with learning rate of 0.0002, β1 = 0.5, β2 = 0.999 and mini-batch size of 48. In the image refining stage, we empirically set λ1 = 1, λ2 = 1, λ3 = 0.01, λ4 = 0.01, λ5 = 0.001. |