  Diffusion is commonly used as a ranking or re-ranking method in retrieval
tasks to achieve higher retrieval performance, and has attracted lots of
attention in recent years. A downside to diffusion is that it performs slowly
in comparison to the naive k-NN search, which causes a non-trivial online
computational cost on large datasets. To overcome this weakness, we propose a
novel diffusion technique in this paper. In our work, instead of applying
diffusion to the query, we pre-compute the diffusion results of each element in
the database, making the online search a simple linear combination on top of
the k-NN search process. Our proposed method becomes 10~ times faster in terms
of online search speed. Moreover, we propose to use late truncation instead of
early truncation in previous works to achieve better retrieval performance.
