论文标题
通过潜在空间中的密度估计，用于分布外检测的元学习
Meta-learning for Out-of-Distribution Detection via Density Estimation in Latent Space
论文作者
论文摘要
已经提出了许多基于神经网络的分布（OOD）检测方法。但是，他们需要每个目标任务的许多培训数据。我们提出了一种简单而有效的元学习方法，可以在目标任务中使用小的分布数据检测OOD。通过提出的方法，通过潜在空间中的密度估计进行OOD检测。所有任务之间共享的神经网络用于灵活地将原始空间中的实例映射到潜在空间。对神经网络进行元学习，以便通过使用与目标任务不同的各种任务来提高预期的OOD检测性能。这种元学习过程使我们能够在潜在空间中获得适当的代表以进行OOD检测。为了进行密度估计，我们使用每个类别的高斯混合模型（GMM）。我们可以通过最大程度地提高可能性，以最大化的可能性使GMM参数以封闭形式的每个任务中的分配数据调整。由于封闭形式的解决方案是可区分的，因此我们可以通过将溶液纳入元学习目标函数，通过随机梯度下降方法有效地元学习神经网络。在使用六个数据集的实验中，我们证明了所提出的方法比现有的元学习和OOD检测方法更好的性能。
Many neural network-based out-of-distribution (OoD) detection methods have been proposed. However, they require many training data for each target task. We propose a simple yet effective meta-learning method to detect OoD with small in-distribution data in a target task. With the proposed method, the OoD detection is performed by density estimation in a latent space. A neural network shared among all tasks is used to flexibly map instances in the original space to the latent space. The neural network is meta-learned such that the expected OoD detection performance is improved by using various tasks that are different from the target tasks. This meta-learning procedure enables us to obtain appropriate representations in the latent space for OoD detection. For density estimation, we use a Gaussian mixture model (GMM) with full covariance for each class. We can adapt the GMM parameters to in-distribution data in each task in a closed form by maximizing the likelihood. Since the closed form solution is differentiable, we can meta-learn the neural network efficiently with a stochastic gradient descent method by incorporating the solution into the meta-learning objective function. In experiments using six datasets, we demonstrate that the proposed method achieves better performance than existing meta-learning and OoD detection methods.
