  People ``understand'' the world via vision, hearing, tactile, and also the
past experience. Human experience can be learned through normal learning (we
call it explicit knowledge), or subconsciously (we call it implicit knowledge).
These experiences learned through normal learning or subconsciously will be
encoded and stored in the brain. Using these abundant experience as a huge
database, human beings can effectively process data, even they were unseen
beforehand. In this paper, we propose a unified network to encode implicit
knowledge and explicit knowledge together, just like the human brain can learn
knowledge from normal learning as well as subconsciousness learning. The
unified network can generate a unified representation to simultaneously serve
various tasks. We can perform kernel space alignment, prediction refinement,
and multi-task learning in a convolutional neural network. The results
demonstrate that when implicit knowledge is introduced into the neural network,
it benefits the performance of all tasks. We further analyze the implicit
representation learnt from the proposed unified network, and it shows great
capability on catching the physical meaning of different tasks. The source code
of this work is at : https://github.com/WongKinYiu/yolor.
