Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Quantification and Analysis of Layer-wise and Pixel-wise Information Discarding
Authors: Haotian Ma, Hao Zhang, Fan Zhou, Yinqing Zhang, Quanshi Zhang
ICML 2022 | Venue PDF | LLM Run Details | Input Tokens: 28,520 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,759 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments have shown the effectiveness of our metrics in analyzing classic DNNs and explaining existing deep-learning techniques. |
| Researcher Affiliation | Academia | 1 Shanghai Jiao Tong University, Shanghai, China 2 Southern University of Science and Technology, Shenzhen, China. |
| Pseudocode | No | The paper describes mathematical formulations and procedures (e.g., Equation (3) and (4)), but does not include any clearly labeled 'Pseudocode' or 'Algorithm' block or figure. |
| Open Source Code | Yes | The code is available at https://github.com/haotianSustc/deepinfo. |
| Open Datasets | Yes | CUB200-2011 dataset (Wah et al., 2011), CIFAR-10 dataset (Krizhevsky, 2009), ISBI cell tracking challenge (WWW, 2012), Image Net dataset (Russakovsky et al., 2015). |
| Dataset Splits | No | No explicit training/validation/test splits (e.g., percentages, sample counts) are provided for the datasets used in the experiments. The paper states 'we used object images cropped by object bounding boxes for both training and testing' but lacks specific split details. |
| Hardware Specification | No | No specific hardware details (such as GPU models, CPU types, or cloud instance specifications) used for running the experiments are mentioned in the paper. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., Python 3.8, PyTorch 1.9) are mentioned in the paper. |
| Experiment Setup | Yes | In order to learn the parameter σ, we used the learning rate 1 10 4, and learned σ for 100 epochs. ... In the following experiments, we set β = 1.5 10 4. |