Closed mangushev closed 5 years ago
Hi, I think, in the early step of training, it is crucial to select negative samples randomly. Because of,
When the performance of the model is not improved, we consider selecting sampling. Because of, Almost all data is learned effectively except the few hard one. For the a lot of samples, anchor-positive similarity (sap) greater than anchor-negative similarity (san), that cause the loss = |san -sap + alpha| ~0 . The model can no longer be trained effectively. So we can choose the samples that makes loss >0 .
Thanks! That clarifies.
Hi, I see in the article for FaceNet. https://blog.csdn.net/baidu_27643275/article/details/79222206 They select all positives, but from negatives that satisfy criteria, they pick at random from the set instead of hardest negatives only. This feels like more representative picking of samples instead of picking only the hardest. Any views on this. Thanks!