Search Results for author:
Found 4 papers, 3 papers with code
Post hoc Explanations may be Ineffective for Detecting Unknown Spurious Correlation
We investigate whether three types of post hoc model explanations--feature attribution, concept activation, and training point ranking--are effective for detecting a model's reliance on spurious signals in the training data.
Debugging Tests for Model Explanations
For several explanation methods, we assess their ability to: detect spurious correlation artifacts (data contamination), diagnose mislabeled training examples (data contamination), differentiate between a (partially) re-initialized model and a trained one (model contamination), and detect out-of-distribution inputs (test-time contamination).
Generative Modeling for Small-Data Object Detection
This paper explores object detection in the small data regime, where only a limited number of annotated bounding boxes are available due to data rarity and annotation expense.
Sanity Checks for Saliency Maps
We find that reliance, solely, on visual assessment can be misleading.
