  Recently neural networks and multiple instance learning are both attractive
topics in Artificial Intelligence related research fields. Deep neural networks
have achieved great success in supervised learning problems, and multiple
instance learning as a typical weakly-supervised learning method is effective
for many applications in computer vision, biometrics, nature language
processing, etc. In this paper, we revisit the problem of solving multiple
instance learning problems using neural networks. Neural networks are appealing
for solving multiple instance learning problem. The multiple instance neural
networks perform multiple instance learning in an end-to-end way, which take a
bag with various number of instances as input and directly output bag label.
All of the parameters in a multiple instance network are able to be optimized
via back-propagation. We propose a new multiple instance neural network to
learn bag representations, which is different from the existing multiple
instance neural networks that focus on estimating instance label. In addition,
recent tricks developed in deep learning have been studied in multiple instance
networks, we find deep supervision is effective for boosting bag classification
accuracy. In the experiments, the proposed multiple instance networks achieve
state-of-the-art or competitive performance on several MIL benchmarks.
Moreover, it is extremely fast for both testing and training, e.g., it takes
only 0.0003 second to predict a bag and a few seconds to train on a MIL
datasets on a moderate CPU.
