One Explanation is Not Enough: Structured Attention Graphs for Image Classification
Authors: Vivswan Shitole, Fuxin Li, Minsuk Kahng, Prasad Tadepalli, Alan Fern
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct a user study comparing the use of SAGs to traditional saliency maps for answering comparative counterfactual questions about image classifications. Our results show that user accuracy is increased significantly when presented with SAGs compared to standard saliency map baselines. |
| Researcher Affiliation | Academia | Oregon State University {shitolev, lif, minsuk.kahng, tadepall, alan.fern}@oregonstate.edu |
| Pseudocode | No | The paper describes algorithms verbally but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Source code for generating SAGs: https://github.com/viv92/structured-attention-graphs |
| Open Datasets | Yes | The Image Net validation dataset of 50, 000 images is used for our analysis. ... ImageNet [7] |
| Dataset Splits | No | The paper analyzes explanations for pre-trained CNNs (VGGNet, ResNet-50) using the ImageNet validation dataset. It does not describe training/validation/test splits for a model trained by the authors. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU or CPU models used for running experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | we set Ph = 0.9 as asufficiently high fraction in our experiments. ... We set the hyperparameter r = 7 in all our experiments. ... We set m = 10 and vary 0 < k < m as hyperparameters. ... We chose q = 15 as a hyperparameter. ... The constant c is set to 3... Pl is set to 40%... |