How do we know when the predictions made by a classifier can be trusted? This is a fundamental problem that also has immense practical applicability, especially in safety-critical areas such as medicine and autonomous driving. The de facto approach of using the classifier's softmax outputs as a proxy for trustworthiness suffers from the over-confidence issue; while the most recent works incur problems such as additional retraining cost and accuracy versus trustworthiness trade-off. In this work, we argue that the trustworthiness of a classifier's prediction for a sample is highly associated with two factors: the sample's neighborhood information and the classifier's output. To combine the best of both worlds, we design a model-agnostic post-hoc approach NeighborAgg to leverage the two essential information via an adaptive neighborhood aggregation. Theoretically, we show that NeighborAgg is a generalized version of a one-hop graph convolutional network, inheriting the powerful modeling ability to capture the varying similarity between samples within each class. We also extend our approach to the closely related task of mislabel detection and provide a theoretical coverage guarantee to bound the false negative. Empirically, extensive experiments on image and tabular benchmarks verify our theory and suggest that NeighborAgg outperforms other methods, achieving state-of-the-art trustworthiness performance.
- Install the dependencies using pip:
$ pip install -r requirement.txtFirst, checkpoints of pre-trained base classifier should be put in the output folder and BASE_CKPT in the bash file should be modified.
The architecture of the base classifier also needs to be put into the folder output .
Besides, base classifiers can also be trained from scratch using neural network architecture in classifiers. After setting paths, run following code:
$ cd script
$ bash train_base_clf.shTo test your model, use the following command:
$ cd scripts
$ bash eval_image.sh
$ bash eval_tabular.sh