A Unified Taylor Framework for Revisiting Attribution Methods
Authors: Huiqi Deng, Na Zou, Mengnan Du, Weifu Chen, Guocan Feng, Xia Hu11462-11469
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we empirically validate the Taylor reformulations, and reveal a positive correlation between the attribution performance and the number of principles followed by the attribution method via benchmarking on real-world datasets. We empirically validate the proposed Taylor reformulations by comparing the attribution results obtained by the original attribution methods and their Taylor reformulations. The experimental results on MNIST show the two attribution results are almost consistent. We also reveal a strong positive correlation between the attribution performance and the number of principles followed by the attribution method via benchmarking on MNIST and Imagenet. |
| Researcher Affiliation | Academia | 1 Sun Yat-Sen University 2 Texas A&M University |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code or provide links to a code repository. |
| Open Datasets | Yes | We conduct the validation experiments on three models: i) Poly, a second-order polynomial model. ii) M-sg, a threelayer multi-layer perceptron (MLP) model with sigmoid activation, iii) C-sg, a three-layer CNN model with sigmoid activation. These models are all trained on MNIST 3 dataset. The footnote for MNIST is "3http://yann.lecun.com/exdb/mnist/". We also benchmark the six attribution methods in terms of infidelity and object localization accuracy on MNIST and Imagenet (Russakovsky et al. 2015). The ImageNet citation is "Russakovsky, O.; Deng, J.; Su, H.; Krause, J.; Satheesh, S.; Ma, S.; Huang, Z.; Karpathy, A.; Khosla, A.; Bernstein, M.; Berg, A. C.; and Fei-Fei, L. 2015. Image Net Large Scale Visual Recognition Challenge. International Journal of Computer Vision (IJCV) 115(3): 211 252. doi:10.1007/s11263015-0816-y." |
| Dataset Splits | Yes | The average percentage change metrics are averaged on 3k validation set. |
| Hardware Specification | No | The paper mentions the models and datasets used for experiments (e.g., 'C-sg model', 'VGG16', 'MNIST', 'Imagenet'), but does not provide any specific hardware details such as CPU/GPU models or memory specifications. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | We conduct the validation experiments on three models: i) Poly, a second-order polynomial model. ii) M-sg, a threelayer multi-layer perceptron (MLP) model with sigmoid activation, iii) C-sg, a three-layer CNN model with sigmoid activation. Occlusion-p is implemented for patch size 2 2 and 4 4. |