Hands-On Transfer Learning with Python

Capsule networks

We have discussed how various CNN architectures evolved, we and have looked at their successive improvements. Can we now apply CNNs for more advanced applications, such as Advanced Driver Assistance Systems (ADAS) and self-driving cars? Can we detect an obstacle, a pedestrian, and other overlapping objects on roads in real-world scenarios and in real time? Maybe not! We are still not quite there. In spite of the tremendous success of CNNs in ImageNet competitions, CNNs still have some severe limitations that restrict their applicability to more advanced, real-world problems. CNNs have poor translational invariance and lack information about orientation (or pose).

Pose information refers to three-dimensional orientation relative ...