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.

利用损失函数的梯度信息进行训练，实现神经网络在离分布数据上的检测，并通过能量采样方法提高模型性能。

梯度正则化的离群样本检测

Gradient-Regularized Out-of-Distribution Detection

Explaining the predictions made by complex machine learning models helps
users to understand and accept the predicted outputs with confidence. One
promising way is to use similarity-based explanation that provides similar
instances as evidence to support model predictions. Several relevance metrics
are used for this purpose. In this study, we investigated relevance metrics
that can provide reasonable explanations to users. Specifically, we adopted
three tests to evaluate whether the relevance metrics satisfy the minimal
requirements for similarity-based explanation. Our experiments revealed that
the cosine similarity of the gradients of the loss performs best, which would
be a recommended choice in practice. In addition, we showed that some metrics
perform poorly in our tests and analyzed the reasons of their failure. We
expect our insights to help practitioners in selecting appropriate relevance
metrics and also aid further researches for designing better relevance metrics
for explanations.

本研究探讨了几种用来解释机器学习模型预测结果的相关性评估指标，并通过三项测试，发现利用损失函数梯度的余弦相似度表现最佳，可作为实践中的推荐选择。同时，研究者还发现某些指标表现不佳，并分析了其失败原因，期望能帮助从业者选择合适的相关性评估指标，同时推动更好的指标研究。