Bias Also Matters: Bias Attribution for Deep Neural Network Explanation
Authors: Shengjie Wang, Tianyi Zhou, Jeff Bilmes
ICML 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In experiments, we show that BBp can generate complementary and highly interpretable explanations.In Table 1, we compare results on the CIFAR-10, CIFAR-100 (Krizhevsky & Hinton, 2009) and Fashion MNIST (Xiao et al., 2017) datasets. |
| Researcher Affiliation | Academia | 1Paul G. Allen School of Computer Science & Engineering 2Department of Electrical & Computer Engineering, University of Washington, Seattle, USA. |
| Pseudocode | Yes | Algorithm 1 Bias Backpropagation (BBp) |
| Open Source Code | No | The paper does not provide any explicit statement about open-source code availability or a link to a code repository for the described methodology. |
| Open Datasets | Yes | We compare results on the CIFAR-10, CIFAR-100 (Krizhevsky & Hinton, 2009) and Fashion MNIST (Xiao et al., 2017) datasets.We test BBp on STL-10 (Coates et al., 2011) and Image Net(ILSVRC2012) (Russakovsky et al., 2015). |
| Dataset Splits | No | In Table 1, we compare results on the CIFAR-10, CIFAR-100 (Krizhevsky & Hinton, 2009) and Fashion MNIST (Xiao et al., 2017) datasets. |
| Hardware Specification | No | The paper does not provide specific details on the hardware used for running experiments, such as exact GPU or CPU models. |
| Software Dependencies | No | The paper does not specify particular software dependencies with version numbers, such as specific deep learning frameworks or libraries. |
| Experiment Setup | Yes | For STL-10, we use a 10-layer convolutional network ((32,3,1), maxpool, (64,3,1), maxpool, (64,3,1), maxpool, (128,3,1), maxpool, (128,3,1), and dense10, where (i,j,k) corresponds to a convolutional layer with i channels, kernel size j and padding k), and for Image Net we use the VGG-11 network of (Simonyan & Zisserman, 2014).For all options of calculating the bias attribution score, the temperature parameter T is set to 1. |