Debugging Tests for Model Explanations
Authors: Julius Adebayo, Michael Muelly, Ilaria Liccardi, Been Kim
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct comprehensive control experiments to assess several feature attribution methods against 4 bugs: spurious correlation artifact , mislabelled training examples, re-initialized weights, and out-of-distribution (OOD) shift. 4. Human Subject Study. We conduct a 54-person IRB-approved study to assess whether end-users can identify defective models with attributions. |
| Researcher Affiliation | Collaboration | Julius Adebayo , Michael Muelly , Ilaria Liccardi , Been Kim {juliusad,licardi}@mit.edu {muelly,beenkim}@google.com Massachusetts Institute of Technology Google Inc |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | Yes | We refer to: https://github.com/adebayoj/explaindebug.git, for code to replicate our findings and experiments. |
| Open Datasets | Yes | We use dog breeds from the Cats-v-Dogs dataset [45] and Bird species from the Caltech-UCSD dataset [66]. |
| Dataset Splits | No | The paper mentions training, validation, and test sets (e.g., 'The model achieves a 93.2, 91.7, 88 percent accuracy on the training, validation, and test sets.'), but it does not specify the exact percentages or counts for these splits required for reproduction. |
| Hardware Specification | No | The paper does not specify any particular hardware used for running the experiments (e.g., specific GPU or CPU models, memory, or cloud instance types). |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies or libraries used in the experiments. |
| Experiment Setup | Yes | We consider a birds-vs-dogs binary classification task. ... train a CNN with 5 convolutional layers and 3 fully-connected layers (we refer to this architecture as BVD-CNN from here on) with Re LU activation functions but sigmoid in the final layer. The model achieves a test accuracy of 94-percent. ... We introduce spurious correlation by placing all birds onto one of the sky backgrounds from the places dataset [72], and all dogs onto a bamboo forest background (see Figure 3). ... We instantiate this bug on a pre-trained VGG-16 model on Imagenet [52]. |