Self-Paced Curriculum Learning
Authors: Lu Jiang, Deyu Meng, Qian Zhao, Shiguang Shan, Alexander Hauptmann
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments We present experimental results for the proposed SPCL on two tasks: matrix factorization and multimedia event detection. We demonstrate that our approach outperforms baseline methods on both tasks. |
| Researcher Affiliation | Academia | 1 School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA, 15217 2 School of Mathematics and Statistics, Xi an Jiaotong University, Xi an, Shaanxi, P. R. China, 710049 3 Institute of Computing Technology, Chinese Academy of Sciences, Beijing, P. R. China, 100190 |
| Pseudocode | Yes | Algorithm 1: Self-paced Curriculum Learning. input : Input dataset D, predetermined curriculum γ, self-paced function f and a stepsize µ output: Model parameter w 1 Derive the curriculum region Ψ from γ; 2 Initialize v , λ in the curriculum region; 3 while not converged do 4 Update w = arg minw E(w, v ; λ, Ψ); 5 Update v = arg minv E(w , v; λ, Ψ); 6 if λ is small then increase λ by the stepsize µ; |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-source code of the proposed methodology. |
| Open Datasets | Yes | TRECVID Multimedia Event Detection (MED) 2013 Development, MED13Test and MED14Test sets were used (Over et al. 2013), which include around 34,000 Internet videos. |
| Dataset Splits | Yes | all parameters were carefully tuned on a validation set on a different set of events. |
| Hardware Specification | No | The paper does not provide specific hardware specifications used for running the experiments. |
| Software Dependencies | No | LM-BFGS (Zhu et al. 1997) in stats package in the R language, and Step 4 was solved by a standard quadratic programming toolkit. |
| Experiment Setup | No | Mixture scheme was used, and all parameters were carefully tuned on a validation set on a different set of events. ... Step 5 was solved by LM-BFGS (Zhu et al. 1997) in stats package in the R language, and Step 4 was solved by a standard quadratic programming toolkit. |