We present local ensembles, a method for detecting underspecification -- when many possible predictors are consistent with the training data and model class -- at test time in a pre-trained model. Our method uses local second-order information to approximate the variance of predictions across an ensemble of models from the same class. We compute this approximation by estimating the norm of the component of a test point's gradient that aligns with the low-curvature directions of the Hessian, and provide a tractable method for estimating this quantity. Experimentally, we show that our method is capable of detecting when a pre-trained model is underspecified on test data, with applications to out-of-distribution detection, detecting spurious correlates, and active learning.
翻译:我们提出本地组合,这是在预培训模型中测试时,当许多可能的预测器与培训数据和模型类一致时,在测试时发现具体度不足的一种方法。我们的方法使用本地二阶信息来估计同一类模型一组数的预测差异。我们计算这一近似值的方法是估算试验点梯度中与赫森人低曲线方向相一致的值,并为估计这一数量提供一个可移植的方法。我们实验性地表明,当预培训模型在测试数据方面没有被指定时,我们的方法能够检测到测试数据,应用到超出分布的检测、检测出错的关联性以及积极学习。