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..
Deep Representation Learning with Target Coding
Authors: Shuo Yang, Ping Luo, Chen Change Loy, Kenneth W. Shum, Xiaoou Tang
AAAI 2015 | Venue PDF | 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 EMAIL" 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. |