Iterative Search Attribution for Deep Neural Networks
Authors: Zhiyu Zhu, Huaming Chen, Xinyi Wang, Jiayu Zhang, Zhibo Jin, Jason Xue, Jun Shen
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experimental results show that our method has superior interpretability in image recognition tasks compared with stateof-the-art baselines. Our code is available at: https://github.com/LMBTough/ISA |
| Researcher Affiliation | Collaboration | 1School of Electrical and Computer Engineering, University of Sydney, Sydney, NSW, Australia 2Faculty of Computer Science & Information Technology, University of Malaya 3Suzhou Yierqi, Suzhou, China 4Data61, CSIRO, Sydney, NSW, Australia 5University of Wollongong, Australia. |
| Pseudocode | Yes | Algorithm 1 Iterative Search Attribution (Appendix J) |
| Open Source Code | Yes | Our code is available at: https://github.com/LMBTough/ISA |
| Open Datasets | Yes | In the experiment, we employ the widely used Image Net (Deng et al., 2009) dataset. |
| Dataset Splits | No | The paper mentions selecting 1000 samples for evaluation, but it does not specify explicit training/validation/test splits (e.g., percentages or counts for training and validation sets) needed to reproduce the training of the models (Inception-v3, ResNet-50, VGG16) used in the experiments. It implicitly uses pre-trained models. |
| Hardware Specification | Yes | We perform the experiments on a platform with a single Nvidia RTX3090 GPU. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies used (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | Specifically, we set the step size to be 5000, ascent step T1 and descent step T2 to be 8 of each, learning rate to 0.002, and S to 1.1. |