struct space中相似度的计算代码和论文不一致 about ada-nets

[Thomas-wyh]

Thank you for your attention to Ada-NETS.

We are using the cosine distance.
Although the faiss returns a Euclidean distance, we have converted it to a cosine distance in here.

You can deduce a formula that when the feature vectors are L2 normalized, the Euclidean distance between two features is twice the corresponding cosine distance.

We use similarity in the Eq.2 to describe the method for convenience, but essentially it is the same as distance (d=1-s).

Let me know if there are any further questions.

[Y1YU]

ok，thanks for your answer.

[Zhubisong]

为什么要jd=1-sim，不应该'sim'越大，两者越相似吗？