Questions tagged [facenet]
6 questions
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How is the constraint $\|f(x)\|_{2}=1$ enforced for the embedding $f(x)$ in the FaceNet paper?
In the FaceNet paper, under section 3.2, the authors mention that:
The embedding is represented by $f(x) \in \mathbb{R}^{d}$. It embeds an image $x$ into a $d$-dimensional Euclidean space. Additionally, we constrain this embedding to live on the…
Abhijit Balaji
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Does the converted (now square) distorted image of a face affect the accuracy of the calculation of the similarity in FaceNet?
As far as I know,
FaceNet requires a square image as an input.
MTCNN can detect and crop the original image as a square, but distortion occurs.
Is it okay to feed the converted (now square) distorted image into FaceNet? Does it affect the…
jjangga
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How do we give a kick start to the Facenet network?
I read the Facenet paper and one thing I am not sure about (it might be trivial and I missed it) is how do we give the kick start to the network.
The embeddings, in the beginning, are random, so picking hard (or semi-hard) negatives, based on the…
Alex Marshall
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How does FaceNet (or similar) bootstrap new faces?
In a metric learning system the system can be trained on known examples such that common classes (faces) are clustered together and separated from each other as much as possible. If triplet loss is used, the variance of each cluster is encouraged to…
Mastiff
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Should I expect the FaceNet to learn to group faces that look different, but in a (probably) predictable way?
A FaceNet learns to cluster images containing the same face together. I want to use a pre-trained FaceNet that was trained to do this, to now learn to cluster faces together, thus clustering clusters of images. More specifically, now that the…
Richie Bendall
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How to retrain a Facenet model with the triplet loss function?
I want to calculate the similarity or distance of two faces. I'm using Python.
I have read and done what this tutorial says. However, the result is not good (the similarity of same faces and similarity of different faces are very very very close to…
behrad
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