Negative Flux Aggregation to Estimate Feature Attributions
Authors: Xin Li, Deng Pan, Chengyin Li, Yao Qiang, Dongxiao Zhu
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Both qualitative and quantitative experiments demonstrate a superior performance of Ne FLAG in generating more faithful attribution maps than the competing methods. |
| Researcher Affiliation | Academia | Xin Li , Deng Pan , Chengyin Li , Yao Qiang and Dongxiao Zhu Department of Computer Science, Wayne State University, USA {xinlee, pan.deng, cli, yao, dzhu}@wayne.edu |
| Pseudocode | Yes | Algorithm 1 Ne FLAG(f, x, n, Sx, ϵ, m) |
| Open Source Code | Yes | Our code is available at https://github.com/xinli0928/Ne FLAG. |
| Open Datasets | Yes | Dataset. The Image Net [Deng et al., 2009] dataset is used for all of our experiments. |
| Dataset Splits | Yes | We randomly select 5,000 samples from Image Net validation dataset with 5 samples from each class as a good representation of the classes. |
| Hardware Specification | No | The paper does not provide specific hardware details such as exact GPU/CPU models, processor types, or memory amounts used for running its experiments. |
| Software Dependencies | No | The paper mentions using 'captum' but does not provide any specific version numbers for software dependencies or libraries. |
| Experiment Setup | Yes | The Ne FLAG is configured as follows: ϵ-sphere radius is set to ϵ = 0.1, and the number of random negative flux point x is n = 20. (...) For AGI, we adopt the default parameter settings reported in [Pan et al., 2021], i.e., step size ϵ = 0.05, number of false classes n = 20. |