The FaceNet algorithm (described in this article) uses a convolutional neural network to represent an image in an 128 dimensional Euclidean space.
While reading the article I didn't understand:
- How does the loss function impact on the convolutional network (in normal networks, in order to minimize the loss the weights are slightly changed - backpropagation - so, what happens in this case?)
how are the triplets chosen?
2.1 . how do I know a negative image is hard
2.2 . why am I using the loss function to determine the negative image
2.3 . when do I check my images for hardness with respect to the anchor - I believe that is before I send a triplet to be processed by the network, right.
