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What does the embedding mean in the FaceNet?



2019 Community Moderator Electionconceptual question about triplet loss embeddingsHow to create a multi-dimensional softmax output in Tensorflow?What are the state-of-the-art models for identifying objects in photos?What does depth mean in the SqueezeNet architectural dimensions table?Keras: Softmax output into embedding layerWhat is the neural network architecture behind Facebook's Starspace model?Face embedding of unseen imagesMake embedding more Gaussian-like










2












$begingroup$


I am reading the paper about FaceNet but I can't get what does the embedding mean in this paper? Is it a hidden layer of the deep CNN?
P.S. English isn't my native language.










share|improve this question









$endgroup$
















    2












    $begingroup$


    I am reading the paper about FaceNet but I can't get what does the embedding mean in this paper? Is it a hidden layer of the deep CNN?
    P.S. English isn't my native language.










    share|improve this question









    $endgroup$














      2












      2








      2





      $begingroup$


      I am reading the paper about FaceNet but I can't get what does the embedding mean in this paper? Is it a hidden layer of the deep CNN?
      P.S. English isn't my native language.










      share|improve this question









      $endgroup$




      I am reading the paper about FaceNet but I can't get what does the embedding mean in this paper? Is it a hidden layer of the deep CNN?
      P.S. English isn't my native language.







      cnn embeddings






      share|improve this question













      share|improve this question











      share|improve this question




      share|improve this question










      asked Nov 13 '18 at 22:50









      ШахШах

      1257




      1257




















          2 Answers
          2






          active

          oldest

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          2












          $begingroup$

          Assume you have features wich lie in a $R^n$ space, e.g. your input is a picture with $28 times 28$ pixels, then $n$ would be $28 times 28 = 784$.



          Now you can "embedd" your features into another $R^d$ space, where often $d < n$. This way you learn a rich representation of your input. When you compress your $784$ input-pixels to, lets say $64$ you compressed your input by more than a factor $10$ and can elimnate redundant/useless features.



          This embedding learning is of course done in such a way, that you could fully restore your original $784$ pixels out of your $64$ compressed pixels.






          share|improve this answer











          $endgroup$












          • $begingroup$
            Thank you for simple and clear explanation, seems I could get it!
            $endgroup$
            – Шах
            Nov 14 '18 at 20:52


















          2












          $begingroup$


          An embedding is a mapping from discrete objects, such as words, to vectors of real numbers.




          - Tensorflow/Embeddings



          With reference to the FaceNet paper, I should say that embedding here simply means the tensor obtained by performing a forward propagation over an image. The obtained embedding of the image and the target image are then compared to find the closeness among the images. The loss function specified by the equation $L=sum_i^N [ ||f(x_i^a)-f(x_i^p)||_2^2-||f(x_i^a)-f(x_i^n)||_2^2+alpha]_+ $ is simply used here to to find the euclidean distance between the generated embedding of the anchor, positive and negative images obtained by performing forward propagation. This technique is also called as Siamese network.



          I suggest you to read the full original FaceNet paper for clearer understanding of the FaceNet architecture and working.






          share|improve this answer











          $endgroup$













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            2 Answers
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            2 Answers
            2






            active

            oldest

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            active

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            active

            oldest

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            2












            $begingroup$

            Assume you have features wich lie in a $R^n$ space, e.g. your input is a picture with $28 times 28$ pixels, then $n$ would be $28 times 28 = 784$.



            Now you can "embedd" your features into another $R^d$ space, where often $d < n$. This way you learn a rich representation of your input. When you compress your $784$ input-pixels to, lets say $64$ you compressed your input by more than a factor $10$ and can elimnate redundant/useless features.



            This embedding learning is of course done in such a way, that you could fully restore your original $784$ pixels out of your $64$ compressed pixels.






            share|improve this answer











            $endgroup$












            • $begingroup$
              Thank you for simple and clear explanation, seems I could get it!
              $endgroup$
              – Шах
              Nov 14 '18 at 20:52















            2












            $begingroup$

            Assume you have features wich lie in a $R^n$ space, e.g. your input is a picture with $28 times 28$ pixels, then $n$ would be $28 times 28 = 784$.



            Now you can "embedd" your features into another $R^d$ space, where often $d < n$. This way you learn a rich representation of your input. When you compress your $784$ input-pixels to, lets say $64$ you compressed your input by more than a factor $10$ and can elimnate redundant/useless features.



            This embedding learning is of course done in such a way, that you could fully restore your original $784$ pixels out of your $64$ compressed pixels.






            share|improve this answer











            $endgroup$












            • $begingroup$
              Thank you for simple and clear explanation, seems I could get it!
              $endgroup$
              – Шах
              Nov 14 '18 at 20:52













            2












            2








            2





            $begingroup$

            Assume you have features wich lie in a $R^n$ space, e.g. your input is a picture with $28 times 28$ pixels, then $n$ would be $28 times 28 = 784$.



            Now you can "embedd" your features into another $R^d$ space, where often $d < n$. This way you learn a rich representation of your input. When you compress your $784$ input-pixels to, lets say $64$ you compressed your input by more than a factor $10$ and can elimnate redundant/useless features.



            This embedding learning is of course done in such a way, that you could fully restore your original $784$ pixels out of your $64$ compressed pixels.






            share|improve this answer











            $endgroup$



            Assume you have features wich lie in a $R^n$ space, e.g. your input is a picture with $28 times 28$ pixels, then $n$ would be $28 times 28 = 784$.



            Now you can "embedd" your features into another $R^d$ space, where often $d < n$. This way you learn a rich representation of your input. When you compress your $784$ input-pixels to, lets say $64$ you compressed your input by more than a factor $10$ and can elimnate redundant/useless features.



            This embedding learning is of course done in such a way, that you could fully restore your original $784$ pixels out of your $64$ compressed pixels.







            share|improve this answer














            share|improve this answer



            share|improve this answer








            edited Nov 15 '18 at 9:32

























            answered Nov 14 '18 at 8:13









            Andreas LookAndreas Look

            431110




            431110











            • $begingroup$
              Thank you for simple and clear explanation, seems I could get it!
              $endgroup$
              – Шах
              Nov 14 '18 at 20:52
















            • $begingroup$
              Thank you for simple and clear explanation, seems I could get it!
              $endgroup$
              – Шах
              Nov 14 '18 at 20:52















            $begingroup$
            Thank you for simple and clear explanation, seems I could get it!
            $endgroup$
            – Шах
            Nov 14 '18 at 20:52




            $begingroup$
            Thank you for simple and clear explanation, seems I could get it!
            $endgroup$
            – Шах
            Nov 14 '18 at 20:52











            2












            $begingroup$


            An embedding is a mapping from discrete objects, such as words, to vectors of real numbers.




            - Tensorflow/Embeddings



            With reference to the FaceNet paper, I should say that embedding here simply means the tensor obtained by performing a forward propagation over an image. The obtained embedding of the image and the target image are then compared to find the closeness among the images. The loss function specified by the equation $L=sum_i^N [ ||f(x_i^a)-f(x_i^p)||_2^2-||f(x_i^a)-f(x_i^n)||_2^2+alpha]_+ $ is simply used here to to find the euclidean distance between the generated embedding of the anchor, positive and negative images obtained by performing forward propagation. This technique is also called as Siamese network.



            I suggest you to read the full original FaceNet paper for clearer understanding of the FaceNet architecture and working.






            share|improve this answer











            $endgroup$

















              2












              $begingroup$


              An embedding is a mapping from discrete objects, such as words, to vectors of real numbers.




              - Tensorflow/Embeddings



              With reference to the FaceNet paper, I should say that embedding here simply means the tensor obtained by performing a forward propagation over an image. The obtained embedding of the image and the target image are then compared to find the closeness among the images. The loss function specified by the equation $L=sum_i^N [ ||f(x_i^a)-f(x_i^p)||_2^2-||f(x_i^a)-f(x_i^n)||_2^2+alpha]_+ $ is simply used here to to find the euclidean distance between the generated embedding of the anchor, positive and negative images obtained by performing forward propagation. This technique is also called as Siamese network.



              I suggest you to read the full original FaceNet paper for clearer understanding of the FaceNet architecture and working.






              share|improve this answer











              $endgroup$















                2












                2








                2





                $begingroup$


                An embedding is a mapping from discrete objects, such as words, to vectors of real numbers.




                - Tensorflow/Embeddings



                With reference to the FaceNet paper, I should say that embedding here simply means the tensor obtained by performing a forward propagation over an image. The obtained embedding of the image and the target image are then compared to find the closeness among the images. The loss function specified by the equation $L=sum_i^N [ ||f(x_i^a)-f(x_i^p)||_2^2-||f(x_i^a)-f(x_i^n)||_2^2+alpha]_+ $ is simply used here to to find the euclidean distance between the generated embedding of the anchor, positive and negative images obtained by performing forward propagation. This technique is also called as Siamese network.



                I suggest you to read the full original FaceNet paper for clearer understanding of the FaceNet architecture and working.






                share|improve this answer











                $endgroup$




                An embedding is a mapping from discrete objects, such as words, to vectors of real numbers.




                - Tensorflow/Embeddings



                With reference to the FaceNet paper, I should say that embedding here simply means the tensor obtained by performing a forward propagation over an image. The obtained embedding of the image and the target image are then compared to find the closeness among the images. The loss function specified by the equation $L=sum_i^N [ ||f(x_i^a)-f(x_i^p)||_2^2-||f(x_i^a)-f(x_i^n)||_2^2+alpha]_+ $ is simply used here to to find the euclidean distance between the generated embedding of the anchor, positive and negative images obtained by performing forward propagation. This technique is also called as Siamese network.



                I suggest you to read the full original FaceNet paper for clearer understanding of the FaceNet architecture and working.







                share|improve this answer














                share|improve this answer



                share|improve this answer








                edited 44 mins ago

























                answered Nov 14 '18 at 20:23









                thanatozthanatoz

                569319




                569319



























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                    ValueError: Expected n_neighbors <= n_samples, but n_samples = 1, n_neighbors = 6 (SMOTE) The 2019 Stack Overflow Developer Survey Results Are InCan SMOTE be applied over sequence of words (sentences)?ValueError when doing validation with random forestsSMOTE and multi class oversamplingLogic behind SMOTE-NC?ValueError: Error when checking target: expected dense_1 to have shape (7,) but got array with shape (1,)SmoteBoost: Should SMOTE be ran individually for each iteration/tree in the boosting?solving multi-class imbalance classification using smote and OSSUsing SMOTE for Synthetic Data generation to improve performance on unbalanced dataproblem of entry format for a simple model in KerasSVM SMOTE fit_resample() function runs forever with no result