# BIMO: Bootstrap Inter-Intra Modality at Once Unsupervised Learning for Multivariate Time Series
## 3 Results and Discussion
### Univariate Time Series
\begin{table}
\begin{tabular}{l c c c c c} \hline  & \multicolumn{2}{c}{**Single**} & \multicolumn{2}{c}{**Plural**} \\
**Dataset** & _Inter_ & _Intra_ & _Inter and Intra_ & _Inter \(\mapsto\) Intra_ & _Intra \(\mapsto\) Inter_ \\  & _Inter_ & _Intra_ & _Inter and Intra_ & _Inter \(\mapsto\) Intra_ & _Intra \(\mapsto\) Inter_ \\  & _Inter_ & _Inter_ & _Inter_ & _Inter_ \(\mapsto\) Intra_ & _Inter_ (BIMO)_ \\ \hline ECG200 & 0.890 & 0.890 & 0.880 & 0.890 & **0.900** \\ ECG5000 & 0.940 & 0.940 & **0.941** & 0.939 & 0.940 \\ ECGFiveDays & 0.991 & 0.998 & 0.995 & 0.997 & **1.000** \\ ElectricDevices & 0.606 & 0.521 & 0.625 & 0.585 & **0.632** \\ FaceAll & 0.830 & 0.680 & 0.771 & 0.701 & **0.839** \\ FaceFour & 0.853 & **0.875** & 0.841 & 0.841 & 0.841 \\ FacesUCR & 0.947 & 0.920 & 0.922 & 0.917 & **0.948** \\ FiftyWords & 0.774 & **0.792** & 0.785 & 0.788 & 0.783 \\ Fish & **0.959** & 0.901 & 0.948 & 0.913 & **0.959** \\ FordA & 0.867 & **0.920** & 0.870 & 0.918 & 0.850 \\ FordB & 0.718 & **0.788** & 0.756 & 0.775 & 0.714 \\ GunPoint & **1.000** & 0.986 & 0.993 & 0.986 & **1.000** \\ Ham & **1.000** & 0.760 & 0.740 & 0.712 & 0.740 \\ HandOutlines & 0.921 & 0.916 & 0.902 & 0.913 & **0.924** \\ Haptics & **0.916** & 0.494 & 0.523 & 0.487 & 0.510 \\ Herring & 0.594 & 0.688 & 0.625 & **0.703** & **0.703** \\ InlineSkate & 0.352 & 0.367 & 0.367 & **0.374** & 0.372 \\ InsWinbeatSound & 0.608 & 0.598 & 0.609 & 0.597 & **0.630** \\ ItalyPowerDemand & 0.955 & 0.954 & 0.952 & **0.963** & **0.963** \\ LarKitAppliances & **0.871** & 0.621 & 0.863 & 0.659 & 0.866 \\ Lightning2 & 0.767 & 0.783 & 0.767 & 0.717 & **0.883** \\ Lightning2 & 0.778 & 0.778 & 0.764 & 0.750 & **0.819** \\ Mallat & 0.898 & 0.829 & 0.920 & 0.875 & **0.956** \\ Meat & **1.000** & 0.950 & 0.983 & 0.983 & **1.000** \\ MedicalImages & 0.733 & 0.726 & 0.730 & **0.746** & 0.730 \\ MidPhaOutAgeGroup & 0.533 & 0.658 & 0.618 & 0.605 & **0.826** \\ MidPhaOutCorrect & 0.799 & 0.792 & 0.799 & **0.823** & 0.618 \\ MiddlePhalanxTW & **0.586** & 0.559 & 0.566 & 0.533 & 0.566 \\ MoteStrain & 0.854 & 0.851 & 0.859 & 0.851 & **0.871** \\ NonInvFetECGTho1 & 0.916 & 0.891 & 0.907 & 0.894 & **0.923** \\ NonInvFetECGTho2 & 0.926 & 0.905 & 0.920 & 0.907 & **0.929** \\ OliveOil & **1.000** & 0.964 & 0.964 & 0.964 & 0.964 \\ OSULeaf & 0.717 & 0.650 & 0.696 & 0.667 & **0.729** \\ PhaOutCorrect & 0.780 & 0.783 & 0.793 & 0.770 & **0.801** \\ Phoneme & 0.249 & 0.216 & **0.275** & 0.220 & 0.263 \\ Plane & **1.000** & 0.990 & 0.990 & **1.000** & **1.000** \\ ProPhaOutAgeGroup & 0.848 & 0.843 & 0.814 & 0.843 & **0.878** \\ ProPhaOutCorrect & 0.885 & 0.882 & **0.892** & 0.865 & 0.863 \\ ProximalPhalanxTW & 0.789 & **0.819** & 0.784 & 0.789 & 0.814 \\ RefrigerationDevices & 0.538 & 0.556 & 0.530 & **0.559** & 0.524 \\ ScreenType & **0.460** & 0.454 & 0.457 & 0.419 & 0.446 \\ ShapeletSim & 0.583 & 0.628 & 0.600 & 0.639 & **0.694** \\ ShapesAll & 0.662 & 0.647 & 0.663 & 0.647 & **0.667** \\ SmaKitAppliances & 0.755 & 0.728 & **0.796** & 0.726 & 0.790 \\ SonAlBORobSur1 & **0.970** & 0.942 & 0.953 & 0.960 & 0.967 \\ SonAlBORobSur2 & 0.853 & 0.860 & 0.843 & **0.873** & 0.858 \\ StarLightCurves & **0.978** & 0.963 & 0.963 & 0.955 & 0.970 \\ Strawberry & 0.962 & 0.943 & **0.965** & 0.948 & 0.962 \\ SwedishLeaf & **0.929** & 0.925 & 0.918 & 0.923 & **0.929** \\ Symbols & 0.929 & 0.935 & 0.945 & 0.933 & **0.960** \\ \hline \end{tabular}
\end{table} 
Table 4: _Cont._Given multiple auxiliary networks, we employed the static and dynamic loss functions. During training, the static loss function had an equal ratio of inter-auxiliary and intra-auxiliary networks (_Inter and Intra_). The dynamic loss function had different ratios of the inter- and intra-auxiliary networks for every epoch. Herein, the main network was initially trained with the inter-auxiliary network at a higher ratio than that of the intra-auxiliary network. Then, the ratio of the intra-auxiliary network was increased gradually (_Inter_\(\mapsto\)_Intra_).