As a widely used weakly supervised learning scheme, modern multiple instance
learning (MIL) models achieve competitive performance at the bag level.
However, instance-level prediction, which is essential for many important
applications, remains largely unsatisfactory. We propose to conduct novel
active deep multiple instance learning that samples a small subset of
informative instances for annotation, aiming to significantly boost the
instance-level prediction. A variance regularized loss function is designed to
properly balance the bias and variance of instance-level predictions, aiming to
effectively accommodate the highly imbalanced instance distribution in MIL and
other fundamental challenges. Instead of directly minimizing the variance
regularized loss that is non-convex, we optimize a distributionally robust bag
level likelihood as its convex surrogate. The robust bag likelihood provides a
good approximation of the variance based MIL loss with a strong theoretical
guarantee. It also automatically balances bias and variance, making it
effective to identify the potentially positive instances to support active
sampling. The robust bag likelihood can be naturally integrated with a deep
architecture to support deep model training using mini-batches of
positive-negative bag pairs. Finally, a novel P-F sampling function is
developed that combines a probability vector and predicted instance scores,
obtained by optimizing the robust bag likelihood. By leveraging the key MIL
assumption, the sampling function can explore the most challenging bags and
effectively detect their positive instances for annotation, which significantly
improves the instance-level prediction. Experiments conducted over multiple
real-world datasets clearly demonstrate the state-of-the-art instance-level
prediction achieved by the proposed model.

提出了一个新颖的主动式深度多实例学习方法，该方法采样少量信息实例进行注释，旨在显著提高实例级预测。通过设计方差正则化损失函数来平衡实例级别预测的偏差和方差，以便有效控制多实例学习中高度不平衡的实例分布及其他基本挑战，并通过分布鲁棒的袋级似然作为其凸替代，使得其能够平衡偏差和方差，通过强大的理论保证提供方差基于 MIL 损失的良好近似。然后，将该鲁棒性袋似然与深度体系结构自然集成，以支持使用正负袋对的小批量进行深度模型训练。最后，开发了一个结合概率向量和预测实例得分的新型 P-F 采样函数，可以探索最具挑战性的袋并有效地检测其正实例进行注释，从而显著改进实例级预测。多个真实世界数据集上的实验清楚地证明了该模型所达到的最先进实例级预测性能。