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..
Iterative Search Attribution for Deep Neural Networks
Authors: Zhiyu Zhu, Huaming Chen, Xinyi Wang, Jiayu Zhang, Zhibo Jin, Jason Xue, Jun Shen
ICML 2024 | Venue PDF | 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. |