Energy-based Hopfield Boosting for Out-of-Distribution Detection
This addresses the critical need for reliable OOD detection in real-world ML deployments, representing a strong incremental advance over existing outlier exposure methods.
The paper tackled the problem of out-of-distribution detection by introducing Hopfield Boosting, which uses modern Hopfield energy to sharpen decision boundaries, achieving state-of-the-art results with FPR95 improvements from 2.28 to 0.92 on CIFAR-10 and from 11.76 to 7.94 on CIFAR-100.
Out-of-distribution (OOD) detection is critical when deploying machine learning models in the real world. Outlier exposure methods, which incorporate auxiliary outlier data in the training process, can drastically improve OOD detection performance compared to approaches without advanced training strategies. We introduce Hopfield Boosting, a boosting approach, which leverages modern Hopfield energy (MHE) to sharpen the decision boundary between the in-distribution and OOD data. Hopfield Boosting encourages the model to concentrate on hard-to-distinguish auxiliary outlier examples that lie close to the decision boundary between in-distribution and auxiliary outlier data. Our method achieves a new state-of-the-art in OOD detection with outlier exposure, improving the FPR95 metric from 2.28 to 0.92 on CIFAR-10 and from 11.76 to 7.94 on CIFAR-100.