Sparsity Conditional Energy Label Distribution Learning for Age Estimation

Authors: Xu Yang, Xin Geng, Deyu Zhou

IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

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. {x.yang,xgeng,d.zhou}@seu.edu.cn
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 first 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 λ is set as 0.005.