Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learning (1803.04765v1)

Abstract: Deep neural networks (DNNs) enable innovative applications of machine learning like image recognition, machine translation, or malware detection. However, deep learning is often criticized for its lack of robustness in adversarial settings (e.g., vulnerability to adversarial inputs) and general inability to rationalize its predictions. In this work, we exploit the structure of deep learning to enable new learning-based inference and decision strategies that achieve desirable properties such as robustness and interpretability. We take a first step in this direction and introduce the Deep k-Nearest Neighbors (DkNN). This hybrid classifier combines the k-nearest neighbors algorithm with representations of the data learned by each layer of the DNN: a test input is compared to its neighboring training points according to the distance that separates them in the representations. We show the labels of these neighboring points afford confidence estimates for inputs outside the model's training manifold, including on malicious inputs like adversarial examples--and therein provides protections against inputs that are outside the models understanding. This is because the nearest neighbors can be used to estimate the nonconformity of, i.e., the lack of support for, a prediction in the training data. The neighbors also constitute human-interpretable explanations of predictions. We evaluate the DkNN algorithm on several datasets, and show the confidence estimates accurately identify inputs outside the model, and that the explanations provided by nearest neighbors are intuitive and useful in understanding model failures.

• The paper introduces the DkNN method that integrates k-NN with multi-layer deep representations to enhance model confidence, interpretability, and robustness.
• The paper employs layer-wise nearest neighbor searches combined with conformal prediction to generate empirical p-values, ensuring reliable decision support.
• The paper demonstrates improved adversarial detection and calibrated confidence on benchmarks like MNIST and SVHN, highlighting its practical impact.

Deep k-Nearest Neighbors: Towards Confident, Interpretable, and Robust Deep Learning

The paper presents a novel hybrid classification approach, the Deep k-Nearest Neighbors (DkNN), designed to enhance the robustness, interpretability, and confidence of deep neural networks (DNNs). Traditional DNNs are often criticized for their vulnerability to adversarial inputs, lack of interpretability, and overconfidence in predictions. This research introduces a novel strategy that integrates k-Nearest Neighbors (k-NN) with the multi-layered representations learned by DNNs during training.

Key Contributions

1. Methodology: The DkNN algorithm performs nearest neighbor searches for each layer's representation of a test input. By assessing the similarity of these representations with those from the training data, it ensures predictions are well-supported by the training manifold.
2. Confidence and Credibility: The approach uses conformal prediction to provide empirical p-values that quantify prediction confidence and credibility. Confidence reflects the likelihood of a prediction being correct, while credibility measures its conformity with the training set.
3. Interpretability: By leveraging nearest neighbors, DkNN naturally provides interpretable explanations. Training instances that resemble the test sample serve as tangible justifications for predictions, helping researchers understand model decisions better.
4. Robustness Against Adversarial Attacks: The algorithm improves resilience to adversarial examples by ensuring robust prediction support across all layers of the neural network. Even when adversarial examples force misclassifications, they often result in low credibility scores, making them identifiable.

Experimental Results

• Datasets and Accuracy: Evaluated on MNIST, SVHN, and GTSRB datasets, the classifiers show comparable accuracy to traditional DNNs while providing recalibrated confidence metrics.
• Out-of-Distribution Samples: DkNN demonstrates superior calibration on out-of-distribution samples compared to softmax outputs, with significantly lower confidence scores in unwarranted cases.
• Adversarial Detection: On adversarial examples, DkNN recovers some accuracy and consistently delivers low credibility scores, indicating its effectiveness in detecting adversarial manipulations. For instance, on the MNIST dataset, the accuracy on adversarial inputs improved significantly, demonstrating the effectiveness of DkNN's robustness.

Implications and Future Directions

The integration of interpretability and enhanced robustness into DkNN extends the applicability of DNNs to domains where model trust and security are critical, such as healthcare and autonomous systems. By ensuring that predictions are backed by the learned manifold, DkNN offers a promising direction in developing more transparent and reliable AI systems.

Future work could further explore defenses against adaptive adversarial attacks and refine the interpretability mechanism to align even better with human reasoning.

Conclusion

The Deep k-Nearest Neighbors framework signifies progress towards addressing core challenges in deep learning: confidence calibration, interpretability, and robustness. By grounding predictions in the structure of the training data, DkNN represents a step towards more secure and interpretable machine learning models. This approach provides valuable insights into improving AI systems' transparency and reliability, a critical endeavor for the advancement of AI research and application.