Deep Representation Learning with Target Coding
Authors: Shuo Yang, Ping Luo, Chen Change Loy, Kenneth W. Shum, Xiaoou Tang
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments are conducted on popular visual benchmark datasets. We performed two sets of experiments to quantitatively evaluate the effectiveness of target coding. |
| Researcher Affiliation | Academia | 1Department of Information Engineering, The Chinese University of Hong Kong 2Shenzhen Key Lab of CVPR, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions "Our implementation is based on Caffe (Jia 2013)" and provides a project website "http://mmlab.ie.cuhk.edu.hk/projects/Target Coding/" but explicitly states "For more technical details of this work, please contact the corresponding author Ping Luo via pluo.lhi@gmail.com" rather than providing direct public access to their code. |
| Open Datasets | Yes | Three popular benchmark datasets were used, i.e. variant of the MNIST dataset with irrelevant backgrounds and rotation, STL-10, and CIFAR-100. Scalability to large number of classes: This part shows that the proposed method scales well to the 1000-category Image Net-2012 dataset... |
| Dataset Splits | Yes | We followed the standard testing protocol and training/test partitions for each dataset. Image Net-2012 dataset, which contains roughly 1.2 million training images, 50,000 validation images, and 150,000 testing images. |
| Hardware Specification | No | The paper does not specify any particular hardware components such as GPU models, CPU types, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper states "Our implementation is based on Caffe (Jia 2013)" but does not provide specific version numbers for Caffe or any other software dependencies. |
| Experiment Setup | No | The paper states "The details of the network parameters are provided in the supplementary material" and mentions setting "hyper-parameters the same and optimally for all methods" without providing the specific values in the main text. |