DevDiary

I finally finished my Master's Thesis in the Computer Vision chair at TUM. In the course of this thesis, I analyzed existing deep learning approaches to predict future frames in videos. Based on these findings and other modern deep learning practices, such as batch normalization, scheduled sampling to improve recurrent network training or ConvLSTMs, we were able to reach or event outperform state-of-the-art performance in future frame generation. So far, many people asked me about the practical application of frame prediction. Unfortunately, it won't tell us the end of any cliff-hanger movie such as Inception, but the main purpose of such a system is not to generate a perfect forecast of the long-term continuation of any movie clip. This completely impossible in my opinion, since there is not always a wrong or right in many situations. A neural network cannot be able to predict every decision made by all objects inside the scene. Furthermore, the pose of the camera or the

I personally think it is time to switch to English. I'm personally not sure why I waited so long for this. But the advantages of using English in my posts are definitely dominant. To name just a few, it obviously reaches more people, as well as helps me to improve my own English writing skills. We never learn out! ;-)