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..
Automatic Gating of Attributes in Deep Structure
Authors: Xiaoming Jin, Tao He, Cheng Wan, Lan Yi, Guiguang Ding, Dou Shen
IJCAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on a manually-labeled subset of Image Net, a-Yahoo and a-Pascal data set justify the superiority of AG-DBN against several baselines including CNN model and other AG-DBN variants. Specifically, it outperforms the CNN model, VGG19, by significantly reducing the classification error from 26.70% to 13.56% on a-Pascal. |
| Researcher Affiliation | Collaboration | Xiaoming Jin1 , Tao He1 , Cheng Wan2, Lan Yi3, Guiguang Ding1, Dou Shen2 1 School of Software, Tsinghua University, Beijing, China 2 Baidu Corporation, Beijing, China 3 Department of Dev Net, Cisco Systems |
| Pseudocode | Yes | Algorithm 1 Overall Learning Algorithm of AG-DBN ... Algorithm 2 Learning the parameters of l-th RBM with given gate matrix |
| Open Source Code | No | The paper provides a link for a dataset (1https://github.com/Tsinghua-IDE/a-Image Net) but no explicit statement or link to the open-source code for the AG-DBN methodology itself. |
| Open Datasets | Yes | a-Image Net Image Net [Russakovsky et al., 2015] is a widely used image data set. ... 1https://github.com/Tsinghua-IDE/a-Image Net ... a-Yahoo data set is collected by [Farhadi et al., 2009] from the Yahoo image search. ... a-Pascal (this data set is also collected by [Farhadi et al., 2009], and attributes in a-Pascal is the same as a-Yahoo). |
| Dataset Splits | No | Validation data are used to help find the best G in N0 rounds. (The paper mentions validation data but does not provide specific split percentages or counts for reproducibility.) |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU models, CPU types, or memory specifications) used for running the experiments. |
| Software Dependencies | No | The paper does not list any specific software dependencies with version numbers (e.g., Python, TensorFlow, or PyTorch versions). |
| Experiment Setup | Yes | Models all consist of 2 hidden layers of size 500 and 300, and an input layer accepting 50*50 gray scale raw image pixels. ... N0 is set to be 30 ... The batch size s is finally set to 50. ... All experimental results are averaged over 5 independent runs. ... 2 hidden layers are both of size 4096 to make the comparison fare. |