---
title: "Multiple Instance Learning"
source: https://www.jemoka.com/posts/kbhmultiple_instance_learning/
---

\begin{equation} B = \qty[(x_1, y_1), \dots, (x_{n}, y_{n})] \end{equation}
where the labels would be:
\begin{equation} C(b) = \begin{cases} 0, if \sum_{i}^{}y_{i} = 0 \\ 1, \text{otherwise} \end{cases} \end{equation}
and then we maxpool
MILFormer MILFormer is a multiple-instance learning scheme which makes predictions over input patches whose output predictions are weighted as multi-distirbution.
