Attribution Eval

---
name: attribution-eval
description: Evaluates the faithfulness and robustness of perturbation-based image attribution methods by measuring how well generated heatmaps localize objects, predict probability drops upon feature removal, and maintain consistency under hyperparameter variations. Use when the user wants to benchmark on ImageNet, Places365, or asks about evaluating this task. Reports deletion metric.
metadata:
  skill_kind: dataset_eval
  source_arxiv: 1910.04256
  bibtex_key: [REDACTED]
  confidence: high
---

# attribution-eval

> Explaining image classifiers by removing input features using generative models — Agarwal et al. (2019) (arXiv:1910.04256, 2019)

## What this evaluates

Evaluates the faithfulness and robustness of perturbation-based image attribution methods by measuring how well generated heatmaps localize objects, predict probability drops upon feature removal, and maintain consistency under hyperparameter variations.

## Datasets

- **ImageNet** — total ?; splits: test (1000)
- **Places365** — total ?; splits: test (1000)

## Metrics

- `deletion metric` **(primary)** — range: percent
  - Area under the curve (AUC) of the target-class prediction probability as input pixels with the highest attribution scores are sequentially zeroed out. Lower scores indicate higher faithfulness.
- `object localization error` — range: percent
  - Derived bounding boxes from heatmap thresholding are compared to ground-truth boxes using Intersection over Union (IoU). Error is calculated by thresholding IoU at 0.5 and averaging across images. Lower is better.
- `saliency metric` — range: other
  - log(max(a, 0.05)) - log(s(x_p)), where a is the patch size ratio and s(x_p) is the classification probability of the most salient patch. Lower scores indicate more accurate explanations.

## Input / output format

**Input**: Original image, classifier model, and ground-truth bounding boxes (for localization). The evaluation takes the attribution heatmap generated by a method as the prediction.

**Output**: Attribution heatmap (2D tensor of same spatial dimensions as input image) indicating pixel importance scores.

## Scoring recipe

```python
def compute_deletion_metric(heatmap, image, classifier, step_size=224*8):
    flat_heatmap = heatmap.flatten()
    sorted_indices = np.argsort(flat_heatmap)[::-1]
    probs = []
    for i in range(0, len(sorted_indices), step_size):
        masked_img = image.copy()
        masked_img.flatten()[sorted_indices[:i]] = 0
        probs.append(classifier.predict(masked_img))
    return np.trapz(probs)
```

## Common pitfalls

- Using naive perturbations (blur, gray, noise) leaves residual discriminative features, artificially inflating attribution accuracy.
- Hyperparameter sensitivity (patch size, superpixel count, mask resolution) drastically alters heatmaps; evaluating at a single setting misrepresents method robustness.
- The saliency metric uses log(max(a, 0.05)) to prevent undefined values for very small patches, which can skew scores if ignored.
- Localization error thresholding IoU at 0.5 and averaging can obscure poor performance on ambiguous or multi-object images.

## Evidence (verbatim from paper)

> Intuitively, if the attributions in an explanation correctly reflect the importance of input pixels, removing the input pixels of highest attributions should cause a substantial probability drop. The deletion metric [42] measures the area under the curve of the target-class probability as we gradually zero out input pixels of the highest attributions in descending order. The deletion scores are widely used to compare attribution methods [43,18,44,45] i.e., lower deletion scores are considered more accurate.

## Citation

```bibtex
@misc{agarwal2019explaining,
  title={Explaining image classifiers by removing input features using generative models},
  author={Agarwal et al. (2019)},
  year={2019},
  note={arXiv:1910.04256}
}
```

- arXiv: 1910.04256