in Equation  \[eq:b\]: $$\small
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\centering
\begin{aligned}
 & L_{final}= \sum \lVert T_s^H - T_o^H \rVert_2^2\\
 &   + \sum \lVert T_s^E - ((\lambda * T_s^E) + ((1-\lambda) * T_o^E)) \rVert_2^2,
  \end{aligned}
  \label{eq:b}$$ where $T_s^H \& T_o^H$ are the student and oracle cells with a high chance of object existences and $T_s^E \& T_o^E$ are the cells with a low expectation. More specifically, the first part on the left side of Eq. \[eq:b\] calculates the MSE between the parts which have high confidence of objects. The second part calculates a modulated MSE between the cells with a low expectation from both the oracle output tensor and the student output tensor. Here, $\lambda$ is the modulation factor. Figure \[Tensor\_copy\] shows the procedure of creating the target tensor.

By using this loss function, the student model will have a lower chance to generate extra false positives. Also, it would not strictly force the student model to mimic the oracle exactly. We aim to partially address the challenges 1) and 4) in Sec. \[distillation\], with such a fast and effective loss function.

Key Frame Selection
-------------------

Another crucial module to enable TKD working properly is a procedure to demonically select the time instances to 