===========

The presented theoretical framework suggests three hypotheses that deserve empirical tests: 1) TKD can perform visual recognition efficiently, without hurting the recognition performance significantly; 2) the novel loss function can improve online training of the decoder; 3) with our TKD frame selector mechanism, the overall system yields the best performance over other key-frame selection mechanisms, by locating the key frames more accurately (frames which training over them can improve TKD accuracy).

To validate these three hypotheses, we evaluate TKD on the Hollywood scene dataset [@marszalek09], YouTube-Objects dataset [@prest2012learning], The Pursuit of Happyness [@Muccino2008] and the office [@Daniels2013]. We have trained all the base models (RetinaNet [@lin2018focal], FasterRCNN [@ren2015faster], Yolo-v3 and Tiny-Yolo [@redmon2018yolov3]) over MS COCO dataset [@lin2014microsoft]. We implemented the TKD as described in Sec. \[sec:TKD\_structure\] with two different configurations. First, we perform the process of inference and distillation sequentially among the same thread; the other way, we perform the distillation in a separate thread and run the student and oracle in parallel, both architecture implemented using the PyTorch environment [@paszke2017automatic]. All experiments are carried out on one single NVIDIA TITAN X Pascal graphics card.

**Hollywood scene dataset [@marszalek09]** has 10 classes of scenes distributed over 1152 video. In 