Average of Pruning: Improving Performance and Stability of Out-of-Distribution Detection

Detecting out-of-distribution (OOD) inputs has been a critical issue for neural networks in the open world. However, the unstable behavior of OOD detection along the optimization trajectory during training has not been explored clearly. In this article, we first find the performance of OOD detection suffers from overfitting and instability during training: 1) the performance could decrease when the training error is near zero and 2) the performance would vary sharply in the final stage of training. Based on our findings, we propose an average of pruning (AoP), consisting of model averaging (MA) and pruning, to mitigate the unstable behaviors. Specifically, MA can help achieve a stable performance by smoothing the landscape, and pruning is theoretically and empirically verified to eliminate overfitting by avoiding redundant features. Comprehensive experiments on various datasets and architectures are conducted to verify the effectiveness of our method.