Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Sparsity Conditional Energy Label Distribution Learning for Age Estimation
Authors: Xu Yang, Xin Geng, Deyu Zhou
IJCAI 2016 | Venue PDF | LLM Run Details | Input Tokens: 15,247 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,349 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiment results in two age datasets show remarkable advantages of the proposed SCE-LDL model over the previous proposed age estimation methods. |
| Researcher Affiliation | Academia | Key Lab of Computer Network and Information Integration (Ministry of Education) School of Computer Science and Engineering, Southeast University, Nanjing 211189, China. EMAIL |
| Pseudocode | Yes | The learning algorithm is given in the Algorithm 1. Algorithm 1 Learning algorithm of SCE-LDL |
| Open Source Code | No | The paper does not provide an explicit statement or link for the release of its source code. |
| Open Datasets | Yes | Two datasets are used in our experiments: MORPH [Ricanek Jr and Tesafaye, 2006] and the dataset provided by Cha Learn [Escalera et al., 2015]. |
| Dataset Splits | Yes | In the ο¬rst experiment, MORPH dataset is randomly split into 10 chunks. Each time, 1 chunk is used as the testing set and the rest 9 chunks are used as the training set. This procedure is repeated 10 folds... There are totally 3,615 human images can be utilized in this dataset: 2,479 in the training set and 1,136 in the validation set. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory, or processing units) used for running the experiments. |
| Software Dependencies | No | The paper mentions using DPM model [Mathias et al., 2014] and a public available facial point detector software [Sun et al., 2013], but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | Some parameters used in the experiments are set as follows: the number of maximum iteration tmax is 30 and the learning rate is 0.1. ... The number of hidden units R is set as 150 and the Ξ» is set as 0.005. |