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..
Dual Semi-Supervised Learning for Facial Action Unit Recognition
Authors: Guozhu Peng, Shangfei Wang8827-8834
AAAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Within-database and cross-database experiments on three benchmark databases demonstrate the superiority of our method in both AU recognition and face synthesis compared to state-of-the-art works. |
| Researcher Affiliation | Academia | Key Lab of Computing and Communication Software of Anhui Province School of Computer Science and Technology, University of Science and Technology of China Hefei, Anhui, P.R.China, 230027 {gzpeng@mail., sfwang@}ustc.edu.cn |
| Pseudocode | Yes | The training procedure is shown as Algorithm 1. |
| Open Source Code | No | The paper does not provide any link or explicit statement about the availability of open-source code for the methodology described. |
| Open Datasets | Yes | Three benchmark databases are used in our experiments. The Extended Cohn-Kanade database (CK+) (Lucey et al. 2010), The MMI database (Pantic et al. 2005), The UNBC-Mc Master Shoulder Pain Expression Archive database (Pain) (Lucey et al. 2011) |
| Dataset Splits | Yes | We conduct within-database experiments via five fold subject-independent cross-validation and cross-database experiments. |
| Hardware Specification | No | The paper does not specify the hardware used for experiments (e.g., CPU, GPU models). |
| Software Dependencies | No | The paper mentions using 'Tensor Flow framework' and 'Adam algorithm' but does not specify any version numbers for these or other software dependencies. |
| Experiment Setup | Yes | We set α = 0.5 in our experiments to balance the distributions of pseudo-tuples generated from C and G. Discriminator D, classifier C, and generator G are parameterized through a four-layer feedforward network. We implement the proposed method using the Tensor Flow framework. Any gradient-based learning rule could be used to update parameters for the optimization method. We use the Adam (Kingma and Ba 2014) algorithm to optimize D, C, and G in our experiments. |