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Pre-trained CNN for one-shot learning



2019 Community Moderator ElectionUsing neural networks to recognize digits in a sceneWhy not use more than 3 hidden layers for MNIST classification?Tool for designing CNN architecturesWhy Deep Reinforcement Learning fails to learn how to play Asteroids?Are Convolutional Neural Networks a one model, multiple output layers.?How to add non-image features along side images as the input of CNNscnn classifier failing on image after contour selectingCNN to learn and visualize 2d featuresConvolutional neural networks for non-image applications?How to implement a Fourier Convolution layer in keras?










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$begingroup$


I'm currently trying to learn one-shot learning using convolutional neural networks. According to this video, the CNN that I use should have been pre-trained on the MNIST. Why must the CNN be pre-trained??










share|improve this question









$endgroup$
















    3












    $begingroup$


    I'm currently trying to learn one-shot learning using convolutional neural networks. According to this video, the CNN that I use should have been pre-trained on the MNIST. Why must the CNN be pre-trained??










    share|improve this question









    $endgroup$














      3












      3








      3





      $begingroup$


      I'm currently trying to learn one-shot learning using convolutional neural networks. According to this video, the CNN that I use should have been pre-trained on the MNIST. Why must the CNN be pre-trained??










      share|improve this question









      $endgroup$




      I'm currently trying to learn one-shot learning using convolutional neural networks. According to this video, the CNN that I use should have been pre-trained on the MNIST. Why must the CNN be pre-trained??







      cnn mnist






      share|improve this question













      share|improve this question











      share|improve this question




      share|improve this question










      asked Aug 16 '18 at 23:49









      sabrinazuraimisabrinazuraimi

      1263




      1263




















          2 Answers
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          active

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          1












          $begingroup$

          Because the layers in the CNNs must be already able to extract features from images.



          Normally this procedure takes thousands of iterations to be completed, so if they were not trained we could't have the concept of one-shot learning.






          share|improve this answer









          $endgroup$




















            0












            $begingroup$

            There is nothing like pre-training on MNIST data.




            You can take any advanced CNN architecture and then fine-tune the network on your dataset. For performing the one-shot learning, the need for fine-tuning or training is must as this will tune your model to understand and emphasise only on the images that the model has been trained upon.



            To simplify, a network trained on faces will be able to recognise only the facial features over other objects in the images which is must for generating the embedding.




            In one-shot learning, the network tries to generate embedding by forward propagation of the image data through the network. The generated embedding must only emphasis on the required part of the image and not onto the useless data in the image data. A pre-trained model in this case is chosen that is capable of selecting the features in a image that are required to be emphasised.



            Let's try to understand it through the example Face Recognition system. In this case, we would require to have a model that has been trained with images of faces so that whenever we try to create a new embedding, the model must know that it has to concentrate its weights on face rather than the noise in the image. Thus when the embedding are generated, the part of the face is concentrated and thus the cosine distances between the embedding could be minimized to uniquely identify the closeness among the 2 images.



            You can get a better understanding of such a network in this video by Andrew Ng.






            share|improve this answer











            $endgroup$













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              2 Answers
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              2 Answers
              2






              active

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              active

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              active

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              1












              $begingroup$

              Because the layers in the CNNs must be already able to extract features from images.



              Normally this procedure takes thousands of iterations to be completed, so if they were not trained we could't have the concept of one-shot learning.






              share|improve this answer









              $endgroup$

















                1












                $begingroup$

                Because the layers in the CNNs must be already able to extract features from images.



                Normally this procedure takes thousands of iterations to be completed, so if they were not trained we could't have the concept of one-shot learning.






                share|improve this answer









                $endgroup$















                  1












                  1








                  1





                  $begingroup$

                  Because the layers in the CNNs must be already able to extract features from images.



                  Normally this procedure takes thousands of iterations to be completed, so if they were not trained we could't have the concept of one-shot learning.






                  share|improve this answer









                  $endgroup$



                  Because the layers in the CNNs must be already able to extract features from images.



                  Normally this procedure takes thousands of iterations to be completed, so if they were not trained we could't have the concept of one-shot learning.







                  share|improve this answer












                  share|improve this answer



                  share|improve this answer










                  answered Sep 19 '18 at 22:11









                  ILM91ILM91

                  935




                  935





















                      0












                      $begingroup$

                      There is nothing like pre-training on MNIST data.




                      You can take any advanced CNN architecture and then fine-tune the network on your dataset. For performing the one-shot learning, the need for fine-tuning or training is must as this will tune your model to understand and emphasise only on the images that the model has been trained upon.



                      To simplify, a network trained on faces will be able to recognise only the facial features over other objects in the images which is must for generating the embedding.




                      In one-shot learning, the network tries to generate embedding by forward propagation of the image data through the network. The generated embedding must only emphasis on the required part of the image and not onto the useless data in the image data. A pre-trained model in this case is chosen that is capable of selecting the features in a image that are required to be emphasised.



                      Let's try to understand it through the example Face Recognition system. In this case, we would require to have a model that has been trained with images of faces so that whenever we try to create a new embedding, the model must know that it has to concentrate its weights on face rather than the noise in the image. Thus when the embedding are generated, the part of the face is concentrated and thus the cosine distances between the embedding could be minimized to uniquely identify the closeness among the 2 images.



                      You can get a better understanding of such a network in this video by Andrew Ng.






                      share|improve this answer











                      $endgroup$

















                        0












                        $begingroup$

                        There is nothing like pre-training on MNIST data.




                        You can take any advanced CNN architecture and then fine-tune the network on your dataset. For performing the one-shot learning, the need for fine-tuning or training is must as this will tune your model to understand and emphasise only on the images that the model has been trained upon.



                        To simplify, a network trained on faces will be able to recognise only the facial features over other objects in the images which is must for generating the embedding.




                        In one-shot learning, the network tries to generate embedding by forward propagation of the image data through the network. The generated embedding must only emphasis on the required part of the image and not onto the useless data in the image data. A pre-trained model in this case is chosen that is capable of selecting the features in a image that are required to be emphasised.



                        Let's try to understand it through the example Face Recognition system. In this case, we would require to have a model that has been trained with images of faces so that whenever we try to create a new embedding, the model must know that it has to concentrate its weights on face rather than the noise in the image. Thus when the embedding are generated, the part of the face is concentrated and thus the cosine distances between the embedding could be minimized to uniquely identify the closeness among the 2 images.



                        You can get a better understanding of such a network in this video by Andrew Ng.






                        share|improve this answer











                        $endgroup$















                          0












                          0








                          0





                          $begingroup$

                          There is nothing like pre-training on MNIST data.




                          You can take any advanced CNN architecture and then fine-tune the network on your dataset. For performing the one-shot learning, the need for fine-tuning or training is must as this will tune your model to understand and emphasise only on the images that the model has been trained upon.



                          To simplify, a network trained on faces will be able to recognise only the facial features over other objects in the images which is must for generating the embedding.




                          In one-shot learning, the network tries to generate embedding by forward propagation of the image data through the network. The generated embedding must only emphasis on the required part of the image and not onto the useless data in the image data. A pre-trained model in this case is chosen that is capable of selecting the features in a image that are required to be emphasised.



                          Let's try to understand it through the example Face Recognition system. In this case, we would require to have a model that has been trained with images of faces so that whenever we try to create a new embedding, the model must know that it has to concentrate its weights on face rather than the noise in the image. Thus when the embedding are generated, the part of the face is concentrated and thus the cosine distances between the embedding could be minimized to uniquely identify the closeness among the 2 images.



                          You can get a better understanding of such a network in this video by Andrew Ng.






                          share|improve this answer











                          $endgroup$



                          There is nothing like pre-training on MNIST data.




                          You can take any advanced CNN architecture and then fine-tune the network on your dataset. For performing the one-shot learning, the need for fine-tuning or training is must as this will tune your model to understand and emphasise only on the images that the model has been trained upon.



                          To simplify, a network trained on faces will be able to recognise only the facial features over other objects in the images which is must for generating the embedding.




                          In one-shot learning, the network tries to generate embedding by forward propagation of the image data through the network. The generated embedding must only emphasis on the required part of the image and not onto the useless data in the image data. A pre-trained model in this case is chosen that is capable of selecting the features in a image that are required to be emphasised.



                          Let's try to understand it through the example Face Recognition system. In this case, we would require to have a model that has been trained with images of faces so that whenever we try to create a new embedding, the model must know that it has to concentrate its weights on face rather than the noise in the image. Thus when the embedding are generated, the part of the face is concentrated and thus the cosine distances between the embedding could be minimized to uniquely identify the closeness among the 2 images.



                          You can get a better understanding of such a network in this video by Andrew Ng.







                          share|improve this answer














                          share|improve this answer



                          share|improve this answer








                          edited 6 mins ago

























                          answered Nov 19 '18 at 6:27









                          thanatozthanatoz

                          534319




                          534319



























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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