Quantitative Performance Assessment of CNN Units via Topological Entropy Calculation
Authors: Yang Zhao, Hao Zhang
ICLR 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 EXPERIMENTS |
| Researcher Affiliation | Academia | Yang Zhao & Hao Zhang Department of Electronic Engineering Tsinghua University zhao-yan18@mails.tsinghua.edu.cn, haozhang@tsinghua.edu.cn |
| Pseudocode | No | The paper describes computational steps and flow, but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statement or link regarding the availability of open-source code for the described methodology. |
| Open Datasets | Yes | For experiments, we use the VGG16 network architecture to perform the image classification task on the Image Net dataset. |
| Dataset Splits | No | The paper refers to 'training set' and 'test set' but does not explicitly provide details about a validation set or specific split percentages for reproduction. |
| Hardware Specification | Yes | All implementations are deployed on the Nvidia-A100 station with a batch size of 512. |
| Software Dependencies | No | The paper mentions using an 'SGD optimizer' but does not provide specific software names with version numbers for reproducibility (e.g., Python, PyTorch/TensorFlow versions, or other libraries). |
| Experiment Setup | Yes | The hyper-parameters are the same with them in the paper (Simonyan & Zisserman, 2014). Also, similar to the setting in the paper, all the images are simply resized to 224 224. |