A method of updating a classifier on-the-fly is provided. The method includes providing a base classifier. The base classifier is a neural network. The method further includes receiving a class and a set of images associated with the class. The method further includes splitting the set of images into an evaluation set and a training set. The method further includes updating the base classifier on-the-fly to provide an updated classifier. Updating the base classifier includes (1) extracting features for each image from the training set from the base classifier; (2) training the updated classifier using the extracted features; and (3) scoring the evaluation set with the updated classifier.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of updating a classifier on-the-fly comprising: providing a base classifier, wherein the base classifier is a neural network; receiving a class and a set of images associated with the class; splitting the set of images into an evaluation set and a training set; and updating the base classifier on-the-fly to provide an updated classifier, wherein updating the base classifier includes (1) extracting features for each image from the training set from the base classifier; (2) training the updated classifier using the extracted features; and (3) scoring the evaluation set with the updated classifier. 2. The method according to claim 1, wherein the received class and set of images are received from a user who specifies images corresponding to the class. 3. The method according to claim 1, wherein receiving the class and the set of images associated with the class comprises: presenting a first screen to a user, the first screen including a prompt for user input; receiving the user input from the user, the user input indicating the class; presenting a second screen to the user, the second screen including a plurality of selectable images; and receiving a selection from the user representing a subset of the plurality of selectable images, the selection indicating the set of images associated with the class. 4. The method according to claim 3, further comprising: in response to a user requesting additional selectable images, presenting a third screen to the user, the third screen including the additional selectable images; and receiving a second selection from the user representing a subset of the additional selectable images, the second selection indicating additional images to be included in the set of images associated with the class. 5. The method according to claim 1, further comprising: determining whether the updated classifier is satisfactory and, in response to determining that the updated classifier is not satisfactory repeating the following steps until the updated classifier is satisfactory: taking the updated classifier as the current base classifier; receiving additional images associated with the class; updating the current base classifier on-the-fly to provide a current updated classifier, wherein updating the current base classifier includes (1) extracting features for each of the additional images from the current base classifier; (2) training the current updated classifier using the extracted features; and (3) scoring the evaluation set with the current updated classifier. 6. The method according to claim 1, wherein extracting features for each image from the training set from the base classifier includes extracting output activation data from final layers of the base classifier. 7. The method according to claim 1, wherein training the updated classifier using the extracted features includes forming a positive and a negative set from the training set and using zero-shot and/or one-shot learning to train the updated classifier using the positive and negative sets. 8. The method according to claim 1, wherein a user is presented with classifier output of a validation set, and can choose to accept the updated classifier, to reject the updated classifier, or to continue updating the updated classifier with additional training data. 9. A device for updating a classifier on-the-fly, comprising: a processor; and a memory coupled to the processor; wherein the processor is configured to: provide a base classifier, wherein the base classifier is a neural network; receive a class and a set of images associated with the class; split the set of images into an evaluation set and a training set; and update the base classifier on-the-fly to provide an updated classifier, wherein updating the base classifier includes (1) extracting features for each image from the training set from the base classifier; (2) training the updated classifier using the extracted features; and (3) scoring the evaluation set with the updated classifier. 10. The device according to claim 9, wherein the received class and set of images are received from a user who specifies images corresponding to the class. 11. The device according to claim 9, wherein receiving the class and the set of images associated with the class comprises: presenting a first screen to a user, the first screen including a prompt for user input; receiving the user input from the user, the user input indicating the class; presenting a second screen to the user, the second screen including a plurality of selectable images; and receiving a selection from the user representing a subset of the plurality of selectable images, the selection indicating the set of images associated with the class. 12. The device according to claim 11, wherein the processor is further configured to: in response to a user requesting additional selectable images, present a third screen to the user, the third screen including the additional selectable images; and receive a second selection from the user representing a subset of the additional selectable images, the second selection indicating additional images to be included in the set of images associated with the class. 13. The device according to claim 9, wherein the processor is further configured to: determine whether the updated classifier is satisfactory and, in response to determining that the updated classifier is not satisfactory repeat the following steps until the updated classifier is satisfactory: take the updated classifier as the current base classifier; receive additional images associated with the class; update the current base classifier on-the-fly to provide a current updated classifier, wherein updating the current base classifier includes (1) extracting features for each of the additional images from the current base classifier; (2) training the current updated classifier using the extracted features; and (3) scoring the evaluation set with the current updated classifier. 14. The device according to claim 9, wherein extracting features for each image from the training set from the base classifier includes extracting output activation data from final layers of the base classifier. 15. The device according to claim 9, wherein training the updated classifier using the extracted features includes forming a positive and a negative set from the training set and using zero-shot and/or one-shot learning to train the updated classifier using the positive and negative sets. 16. The device according to claim 9, wherein the processor is further configured to present a user with classifier output of a validation set, such that the user can choose to accept the updated classifier, to reject the updated classifier, or to continue updating the updated classifier with additional training data.