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

Detecting out-of-distribution (OOD) data has become a critical component in
ensuring the safe deployment of machine learning models in the real world.
Existing OOD detection approaches primarily rely on the output or feature space
for deriving OOD scores, while largely overlooking information from the
gradient space. In this paper, we present GradNorm, a simple and effective
approach for detecting OOD inputs by utilizing information extracted from the
gradient space. GradNorm directly employs the vector norm of gradients,
backpropagated from the KL divergence between the softmax output and a uniform
probability distribution. Our key idea is that the magnitude of gradients is
higher for in-distribution (ID) data than that for OOD data, making it
informative for OOD detection. GradNorm demonstrates superior performance,
reducing the average FPR95 by up to 16.33% compared to the previous best
method.

本文提出了一种名为 GradNorm 的基于梯度空间的方法，通过利用从梯度空间提取的信息来检测异常数据，可以降低平均误警率（FPR95）高达 16.33％，相对于现有最佳方法效果更好。

探测野外分布移变的梯度重要性

On the Importance of Gradients for Detecting Distributional Shifts in  the Wild

Estimating out-of-distribution (OOD) uncertainty is a central challenge for
safely deploying machine learning models in the open-world environment.
Improved methods for OOD detection in multi-class classification have emerged,
while OOD detection methods for multi-label classification remain underexplored
and use rudimentary techniques. We propose JointEnergy, a simple and effective
method, which estimates the OOD indicator scores by aggregating energy scores
from multiple labels. We show that JointEnergy can be mathematically
interpreted from a joint likelihood perspective. Our results show consistent
improvement over previous methods that are based on the maximum-valued scores,
which fail to capture joint information from multiple labels. We demonstrate
the effectiveness of our method on three common multi-label classification
benchmarks, including MS-COCO, PASCAL-VOC, and NUS-WIDE. We show that
JointEnergy can reduce the FPR95 by up to 10.05% compared to the previous best
baseline, establishing state-of-the-art performance.

我们提出了一种名为 JointEnergy 的简单有效方法，可以有效降低在多标签分类中的误假阳性率，该方法从多个标签的能量得分中聚合获得 OOD 指示器分数。