  Video frame interpolation involves the synthesis of new frames from existing
ones. Convolutional neural networks (CNNs) have been at the forefront of the
recent advances in this field. One popular CNN-based approach involves the
application of generated kernels to the input frames to obtain an interpolated
frame. Despite all the benefits interpolation methods offer, many of these
networks require a lot of parameters, with more parameters meaning a heavier
computational burden. Reducing the size of the model typically impacts
performance negatively. This paper presents a method for parameter reduction
for a popular flow-less kernel-based network (Adaptive Collaboration of Flows).
Through our technique of removing the layers that require the most parameters
and replacing them with smaller encoders, we reduce the number of parameters of
the network and even achieve better performance compared to the original
method. This is achieved by deploying rotation to force each individual encoder
to learn different features from the input images. Ablations are conducted to
justify design choices and an evaluation on how our method performs on
full-length videos is presented.
