# Regularized Instance Embedding for Deep Multi-Instance Learning
## 4 Experiments
### Results
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Dataset** & **Fox** & **Tiger** & **Elephant** \\ \hline mi-SVM [15] & 0.582 & 0.784 & 0.822 \\ MI-SVM [15] & 0.578 & 0.840 & 0.843 \\ MI-Kernel [37] & 0.603 & 0.842 & 0.843 \\ EM-DD [38] & \(0.609\pm 0.101\) & \(0.730\pm 0.096\) & \(0.771\pm 0.097\) \\ mi-Graph [8] & \(0.620\pm 0.098\) & \(0.860\pm 0.083\) & \(0.869\pm 0.078\) \\ miVLAD [51] & \(0.620\pm 0.098\) & \(0.811\pm 0.087\) & \(0.850\pm 0.080\) \\ miFV [51] & \(0.621\pm 0.109\) & \(0.813\pm 0.083\) & \(0.852\pm 0.081\) \\ MIhD [40] & \(0.651\pm 0.011\) & \(0.819\pm 0.021\) & \(0.857\pm 0.018\) \\ MI-Net [5] & \(0.627\pm 0.080\) & \(0.832\pm 0.087\) & \(0.891\pm 0.074\) \\ mi-Net [5] & \(0.622\pm 0.137\) & \(0.861\pm 0.091\) & \(0.874\pm 0.078\) \\ \hline RIE & \(0.686\pm 0.117\) & \(0.885\pm 0.100\) & \(0.907\pm 0.070\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Classification accuracy of different methods for bag classification on Fox, Tiger, and Elephant (task: image classification). \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Dataset** & **MI-Kernel [37]** & **mi-Graph [8]** & **miFV [51]** & **MinD [40]** & **MI-Net [5]** & **mi-Net [5]** & **RIE** \\ \hline alt.atheism & 0.602 & 0.655 & 0.848 & 0.861 & 0.846 & 0.844 & 0.906 \\ comp.graphics & 0.470 & 0.778 & 0.594 & 0.825 & 0.831 & 0.833 & 0.857 \\ comp.windows.misc & 0.510 & 0.631 & 0.615 & 0.730 & 0.730 & 0.730 & 0.777 \\ comp.ibm.pc.hardware & 0.469 & 0.595 & 0.665 & 0.780 & 0.803 & 0.796 & 0.814 \\ comp.sys.mac.hardware & 0.445 & 0.617 & 0.660 & 0.835 & 0.811 & 0.801 & 0.862 \\ comp.window.x & 0.508 & 0.698 & 0.768 & 0.785 & 0.836 & 0.832 & 0.886 \\ misc.forsale & 0.518 & 0.552 & 0.565 & 0.729 & 0.707 & 0.715 & 0.773 \\ rec.autos & 0.529 & 0.720 & 0.667 & 0.775 & 0.812 & 0.806 & 0.835 \\ rec.motorcycles & 0.506 & 0.640 & 0.802 & 0.577 & 0.853 & 0.849 & 0.914 \\ rec.sport.baseball & 0.517 & 0.647 & 0.779 & 0.837 & 0.871 & 0.872 & 0.899 \\ rec.sport.hockey & 0.513 & 0.850 & 0.823 & 0.833 & 0.918 & 0.915 & 0.952 \\ sci.crypt & 0.563 & 0.696 & 0.760 & 0.768 & 0.808 & 0.799 & 0.843 \\ sci.electronics & 0.506 & 0.871 & 0.555 & 0.940 & 0.928 & 0.931 & 0.938 \\ sci.med & 0.506 & 0.621 & 0.783 & 0.832 & 0.862 & 0.864 & 0.895 \\ sci.space & 0.547 & 0.757 & 0.818 & 0.796 & 0.820 & 0.808 & 0.883 \\ soc.religion.christian & 0.492 & 0.590 & 0.814 & 0.841 & 0.829 & 0.823 & 0.875 \\ talk.politics.guns & 0.477 & 0.585 & 0.747 & 0.806 & 0.782 & 0.771 & 0.829 \\ talk.politics.mideast & 0.559 & 0.736 & 0.793 & 0.830 & 0.825 & 0.834 & 0.889 \\ talk.politics.misc & 0.515 & 0.704 & 0.697 & 0.720 & 0.748 & 0.758 & 0.848 \\ talk.religion.misc & 0.554 & 0.633 & 0.739 & 0.725 & 0.778 & 0.776 & 0.829 \\ \hline average & 0.515 & 0.679 & 0.726 & 0.791 & 0.820 & 0.818 & 0.865 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Classification accuracy of different methods for bag classification on 20 Newsgroup datasets (task: text categorization). Table 3 lists the results of our RIE method on the MUSK dataset compared with the experiment results of some classical methods. From the experiment results, we can see that our method was significantly improved on the MUSK dataset, which shows that our method is valid. Table 4 shows the experiment results and comparison on three datasets: Fox, Tiger, and Elephant. RIE achieved the best results in these image datasets, and had significantly better accuracy compared with the other methods of multi-instance learning. The extensive applicability of our RIE method was further proved. Table 5 lists the experiment results on 20 Newsgroups datasets. Compared with the previous methods of the 20 datasets, average accuracy was obviously improved, making our method also useful in text classification. From the results in Table 6, we can observe that our RIE method generally worked better than the other methods, having obtained the best result on average. **Author Contributions:** Conceptualization, Y.L. and H.Z. ; methodology, Y.L. and H.Z. ; software, Y.L. ; validation, Y.L. ; formal analysis, Y.L.