Knowledge-Aware Neuron Interpretation for Scene Classification
Authors: Yong Guan, Freddy Lécué, Jiaoyan Chen, Ru Li, Jeff Z. Pan
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results show that our method, integrating complete concepts, achieves better results than the existing methods. |
| Researcher Affiliation | Academia | 1 Department of Computer Science and Technology, Tsinghua University, China 2School of Computer & Information Technology, Shanxi University, China 3Inria, France 4Department of Computer Science, The University of Manchester, UK 5School of Informatics, The University of Edinburgh, UK |
| Pseudocode | No | The paper describes methods using mathematical formulations and descriptive text, but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code and data are available at: https://github.com/neuroninterpretation/EIIC |
| Open Datasets | Yes | For testing, we use two scene datasets ADE20k (Zhou et al. 2017) and Opensurfaces (Bell, Bala, and Snavely 2014). |
| Dataset Splits | No | For testing, we use two scene datasets ADE20k (Zhou et al. 2017) and Opensurfaces (Bell, Bala, and Snavely 2014). We randomly select 1000 samples from the ADE20k data for the experiment by considering the effect of time efficiency. |
| Hardware Specification | No | The paper describes experiments and results but does not specify any hardware details like CPU, GPU models, or memory. |
| Software Dependencies | No | The paper mentions various models and techniques (e.g., ResNet, DenseNet, AlexNet, MobileNet, Trans E, SVM) but does not provide specific version numbers for any software dependencies used in the experiments. |
| Experiment Setup | No | The paper describes the proposed methods and evaluation metrics but does not provide specific experimental setup details such as hyperparameters (e.g., learning rate, batch size, epochs) or specific training configurations. |