A method for assigning a personalized aesthetic score to an image is provided. The method includes providing a base neural network for generating learned features. The base neural network is trained on a first set of training images and the base neural network includes two or more layers comprising one or more initial layers and one or more final layers. The method further includes receiving a second set of training images and updating the base neural network to generate a personalized neural network based on the received second set of training images. Updating the base neural network comprises re-training the final layers of the base neural network with the second set of images and keeping the initial layers of the base neural network, such that the personalized neural network includes two or more layers comprising one or more initial layers and one or more final layers.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for assigning a personalized aesthetic score to an image, comprising: providing a base neural network for generating learned features, wherein the base neural network is trained on a first set of training images and the base neural network includes two or more layers comprising one or more initial layers and one or more final layers; providing a base machine-learned model for assigning an aesthetic score to an image, wherein a more aesthetically-pleasing image is given a higher aesthetic score and a less aesthetically-pleasing image is given a lower aesthetic score; receiving a second set of training images; updating the base neural network to generate a personalized neural network based on the received second set of training images such that images similar to the received second set of training images will be given a higher aesthetic score when using the personalized neural network than when using the base neural network; receiving an image for scoring; executing the personalized neural network on the received image to generate learned features; and applying the machine-learned model to the received image thereby assigning an aesthetic score to the received image, wherein the learned features are inputs to the machine-learned model, wherein updating the base neural network comprises re-training the final layers of the base neural network with the second set of images and keeping the initial layers of the base neural network, such that the personalized neural network includes two or more layers comprising one or more initial layers and one or more final layers, and the initial layers of the personalized neural network corresponds to the initial layers of the base neural network. 2. The method of claim 1, wherein the final layers of the personalized neural network comprise linear and/or non-linear multi-dimensionality reduction functions. 3. The method of claim 2, wherein the linear and/or non-linear multi-dimensionality reduction functions are trained using stochastic gradient methods, wherein a computation of a gradient is adapted and weighted based on past knowledge bases. 4. The method of claim 2, wherein the final layers of the personalized neural network comprise non-linear multi-dimensionality reduction functions, and the non-linear multi-dimensionality reduction functions are learned from data by minimizing a cost function in a latent feature space. 5. The method of claim 2, wherein the final layers of the personalized neural network comprise non-linear multi-dimensionality reduction functions, and the non-linear multi-dimensionality reduction functions are learned by using a small neural network. 6. The method of claim 2, wherein the final layers of the personalized neural network comprise non-linear multi-dimensionality reduction functions, and the non-linear multi-dimensionality reduction functions are learned by using a Gaussian process. 7. The method of claim 2, wherein the linear and/or non-linear multi-dimensionality reduction functions are learned in near real-time. 8. The method of claim 1, further comprising: receiving a third set of training images; updating the personalized neural network to generate a second personalized neural network based on the received third set of images such that images similar to the received third set of images will be given a higher aesthetic score when using the second personalized neural network than when using the personalized neural network; receiving a second image for scoring; executing the personalized neural network on the received second image to generate second learned features; and applying the machine-learned model to the received second image thereby assigning a second aesthetic score to the received second image, wherein the learned second features are inputs to the machine-learned model, wherein updating the personalized neural network comprises re-training the final layers of the personalized neural network with the third set of images and keeping the initial layers of the personalized neural network, such that the second personalized neural network includes two or more layers comprising one or more initial layers and one or more final layers, and the initial layers of the second personalized neural network corresponds to the initial layers of the personalized neural network. 9. The method of claim 1, wherein receiving the second set of training images comprises: providing a graphical user interface (GUI) to a user comprising one or more candidate images; and receiving input from the user indicating one or more of: (i) that the user prefers an image of the one or more candidate images; (ii) that the user dislikes an image of the one or more candidate images; and (iii) that the user prefers one image over another image of the one or more candidate images. 10. The method of claim 9, further comprising: after updating the base neural network, providing a second GUI to a user comprising one or more second candidate images, the one or more second candidate images being selected based on the personalized neural network; receiving second input from the user indicating one or more of: (i) that the user prefers an image of the one or more second candidate images; (ii) that the user dislikes an image of the one or more second candidate images; and (iii) that the user prefers one image over another image of the one or more second candidate images; generating a third set of training images based on the received second input; updating the personalized neural network to generate a second personalized neural network based on the received third set of images such that images similar to the received third set of images will be given a higher aesthetic score when using the second personalized neural network than when using the personalized neural network; receiving a second image for scoring; executing the personalized neural network on the received second image to generate second learned features; and applying the machine-learned model to the received second image thereby assigning a second aesthetic score to the received second image, wherein the learned second features are inputs to the machine-learned model, wherein updating the personalized neural network comprises re-training the final layers of the personalized neural network with the third set of images and keeping the initial layers of the personalized neural network, such that the second personalized neural network includes two or more layers comprising one or more initial layers and one or more final layers, and the initial layers of the second personalized neural network corresponds to the initial layers of the personalized neural network. 11. A device for assigning a personalized aesthetic score to an image, comprising: a processor; and a memory coupled to the processor; wherein the processor is configured to: provide a base neural network for generating learned features, wherein the base neural network is trained on a first set of training images and the base neural network includes two or more layers comprising one or more initial layers and one or more final layers; provide a base machine-learned model for assigning an aesthetic score to an image, wherein a more aesthetically-pleasing image is given a higher aesthetic score and a less aesthetically-pleasing image is given a lower aesthetic score; receive a second set of training images; update the base neural network to generate a personalized neural network based on the received second set of training images such that images similar to the received second set of training images will be given a higher aesthetic score when using the personalized neural network than when using the base neural network; receive an image for scoring; execute the personalized neural network on the received image to generate learned features; and apply the machine-learned model to the received image thereby assigning an aesthetic score to the received image, wherein the learned features are inputs to the machine-learned model, wherein updating the base neural network comprises re-training the final layers of the base neural network with the second set of images and keeping the initial layers of the base neural network, such that the personalized neural network includes two or more layers comprising one or more initial layers and one or more final layers, and the initial layers of the personalized neural network corresponds to the initial layers of the base neural network. 12. The device of claim 11, wherein the final layers of the personalized neural network comprise linear and/or non-linear multi-dimensionality reduction functions. 13. The device of claim 12, wherein the linear and/or non-linear multi-dimensionality reduction functions are trained using stochastic gradient methods, wherein a computation of a gradient is adapted and weighted based on past knowledge bases. 14. The device of claim 12, wherein the final layers of the personalized neural network comprise non-linear multi-dimensionality reduction functions, and the non-linear multi-dimensionality reduction functions are learned from data by minimizing a cost function in a latent feature space. 15. The device of claim 12, wherein the final layers of the personalized neural network comprise non-linear multi-dimensionality reduction functions, and the non-linear multi-dimensionality reduction functions are learned by using a small neural network. 16. The device of claim 12, wherein the final layers of the personalized neural network comprise non-linear multi-dimensionality reduction functions, and the non-linear multi-dimensionality reduction functions are learned by using a Gaussian process. 17. The device of claim 12, wherein the linear and/or non-linear multi-dimensionality reduction functions are learned in near real-time. 18. The device of claim 11, wherein the processor is further configured to: receive a third set of training images; update the personalized neural network to generate a second personalized neural network based on the received third set of images such that images similar to the received third set of images will be given a higher aesthetic score when using the second personalized neural network than when using the personalized neural network; receive a second image for scoring; execute the personalized neural network on the received second image to generate second learned features; and apply the machine-learned model to the received second image thereby assigning a second aesthetic score to the received second image, wherein the learned second features are inputs to the machine-learned model, wherein updating the personalized neural network comprises re-training the final layers of the personalized neural network with the third set of images and keeping the initial layers of the personalized neural network, such that the second personalized neural network includes two or more layers comprising one or more initial layers and one or more final layers, and the initial layers of the second personalized neural network corresponds to the initial layers of the personalized neural network. 19. The device of claim 11, wherein receiving the second set of training images comprises: providing a graphical user interface (GUI) to a user comprising one or more candidate images; and receiving input from the user indicating one or more of: (i) that the user prefers an image of the one or more candidate images; (ii) that the user dislikes an image of the one or more candidate images; and (iii) that the user prefers one image over another image of the one or more candidate images. 20. The device of claim 19, wherein the processor is further configured to: after updating the base neural network, provide a second GUI to a user comprising one or more second candidate images, the one or more second candidate images being selected based on the personalized neural network; receive second input from the user indicating one or more of: (i) that the user prefers an image of the one or more second candidate images; (ii) that the user dislikes an image of the one or more second candidate images; and (iii) that the user prefers one image over another image of the one or more second candidate images; generate a third set of training images based on the received second input; update the personalized neural network to generate a second personalized neural network based on the received third set of images such that images similar to the received third set of images will be given a higher aesthetic score when using the second personalized neural network than when using the personalized neural network; receive a second image for scoring; execute the personalized neural network on the received second image to generate second learned features; and apply the machine-learned model to the received second image thereby assigning a second aesthetic score to the received second image, wherein the learned second features are inputs to the machine-learned model, wherein updating the personalized neural network comprises re-training the final layers of the personalized neural network with the third set of images and keeping the initial layers of the personalized neural network, such that the second personalized neural network includes two or more layers comprising one or more initial layers and one or more final layers, and the initial layers of the second personalized neural network corresponds to the initial layers of the personalized neural network.