Gradient-Regularized Out-of-Distribution Detection
Sina Sharifi Taha Entesari Bardia Safaei Vishal M. Patel Mahyar Fazlyab
One of the challenges for neural networks in real-life applications is the
overconfident errors these models make when the data is not from the original
training distribution.
  Addressing this issue is known as Out-of-Distribution (OOD) detection.
  Many state-of-the-art OOD methods employ an auxiliary dataset as a surrogate
for OOD data during training to achieve improved performance.
  However, these methods fail to fully exploit the local information embedded
in the auxiliary dataset.
  In this work, we propose the idea of leveraging the information embedded in
the gradient of the loss function during training to enable the network to not
only learn a desired OOD score for each sample but also to exhibit similar
behavior in a local neighborhood around each sample.
  We also develop a novel energy-based sampling method to allow the network to
be exposed to more informative OOD samples during the training phase. This is
especially important when the auxiliary dataset is large. We demonstrate the
effectiveness of our method through extensive experiments on several OOD
benchmarks, improving the existing state-of-the-art FPR95 by 4% on our ImageNet
experiment.
  We further provide a theoretical analysis through the lens of certified
robustness and Lipschitz analysis to showcase the theoretical foundation of our
work. We will publicly release our code after the review process.