Patent Publication Number: US-10331675-B2

Title: Systems and methods for learning new trained concepts used to retrieve content relevant to the concepts learned

Description:
FIELD OF THE DISCLOSURE 
     The present invention relates to systems and methods for learning new trained concepts used to retrieve content relevant to the concepts learned. 
     BACKGROUND 
     Digital media is frequently created by users in the forms of photos, videos, written text, and/or other forms of digital media. This information or content is generally stored, shared, accessed, and/or analyzed by systems throughout the world. Users rely on these systems to search large quantities of information, explore the information, and share it. 
     Information used to expose this information is typically either provided by other users or created by automated systems generally using a fixed set of recognizable patterns. An example of these patterns may be a dog in a picture, the genre of music in a song, or the sentiment of a snippet of text. However, even with sophisticated systems able to recognize patterns, these systems may fail to meet the expectations of users who have specific criteria in mind for retrieving the content they desire. 
     SUMMARY 
     Exemplary implementations of the disclosure may overcome the shortcomings of existing systems by facilitating the learning of new classification systems for discriminating information and content of interest from other content contained in a storage system or data stream. 
     One or more aspects of the disclosure relate to a system configured for learning new trained concepts used to retrieve content relevant to the concepts learned. The system may comprise one or more hardware processors configured by machine-readable instructions to obtain one or more digital media items; obtain an indication conveying a concept to be learned from the one or more digital media items; receive feedback associated with individual ones of the one or more digital media items. The feedback may be based on selection of one or more positive examples of the concept to be learned from the one or more digital media items, selection of one or more negative examples of the concept to be learned from the one or more digital media items, and/or a combination thereof. A given positive example may be a digital media item comprising the concept to be learned. A given negative example may be a digital media item lacking the concept to be learned. The one or more hardware processors are further configured by machine-readable instructions to obtain individual representations for the individual ones of the one or more digital media items. The individual representations may include one or more of machine learning representations, training systems representations, neural network representations, and/or other computing platform representations for the individual ones of the one or more digital media items. In some implementations, the one or more hardware processors may be further configured by machine-readable instructions to determine a trained concept based on the feedback, the individual representations of the one or more digital media items, and/or a combination thereof. The trained concept may be usable for retrieving digital media items relevant to the concept to be learned. 
     One or more aspects of the disclosure relate to a method for learning new trained concepts used to retrieve content relevant to the concepts learned with a system comprising one or more hardware processors. The method may include obtaining one or more digital media items; obtaining an indication conveying a concept to be learned from the one or more digital media items; and receiving feedback associated with individual ones of the one or more digital media items. The feedback may be based on selection of one or more positive examples of the concept to be learned from the one or more digital media items, selection of one or more negative examples of the concept to be learned from the one or more digital media items, and/or a combination thereof. In some implementations, a given positive example may include one or more of a digital media item comprising the concept to be learned, a digital media item related to the concept to be learned, a digital media item similar to the concept to be learned, a digital media item similar to other digital media items comprising the concept to be known, and/or other positive examples of the concept to be learned. Negative examples may include digital media items not comprising, not related, and/or not relevant to the concept to be learned. In some implementations, digital media items may be clustered by similarity. The clusters of digital media items may be labeled as positive examples or negative examples. In some implementations, the method may further include obtaining individual neural network representations for the individual ones of the one or more digital media items. A given neural network representation may include one or more neural network layers. The method may further include determining a trained concept based on the feedback and the neural network representations of the one or more digital media items. The trained concept may be usable for retrieving digital media items relevant to the concept to be learned. 
     These and other features and characteristics of the present technology, as well as the methods of operation and functions of the related elements of structure and the combination of parts and economies of manufacture, will become more apparent upon consideration of the following description and the appended claims with reference to the accompanying drawings, all of which form a part of this specification, wherein like reference numerals designate corresponding parts in the various figures. It is to be expressly understood, however, that the drawings are for the purpose of illustration and description only and are not intended as a definition of the limits of the invention. As used in the specification and in the claims, the singular forms of “a”, “an”, and “the” include plural referents unless the context clearly dictates otherwise. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  illustrates a system configured for learning new trained concepts used to retrieve content relevant to the concepts learned, in accordance with one or more implementations. 
         FIG. 2  illustrates an electronic storage system, in accordance with one or more implementations. 
         FIG. 3  illustrates a system configured for learning new concepts based on information and feedback from a computing platform, in accordance with one or more implementations. 
         FIG. 4  illustrates a system configured for learning new trained concepts, in accordance with one or more implementations 
         FIG. 5  illustrates a method for learning new trained concepts used to retrieve content relevant to the concepts learned, in accordance with one or more implementations. 
     
    
    
     DETAILED DESCRIPTION 
       FIG. 1  illustrates a system  100  configured for learning new trained concepts used to retrieve content relevant to the concepts learned, in accordance with one or more implementations. In some implementations, as shown in this example, system  100  may include one or more of servers  102 , one or more computing platforms  130 , one or more external resources  140 , and/or other components. 
     Server(s)  102  may include electronic storage  104 , one or more processors  106 , and/or other components. Processor(s)  106  may be configured by machine-readable instructions  107 . The machine-readable instructions  107  may include one or more of a digital media component  108 , a concept component  110 , a feedback component  112 , a neural machine learning component  114 , a training component  116 , and/or other components. 
     Digital media component  108  may be configured to obtain one or more digital media items. Digital media items may be any type of content that exists in the form of digital information. For example, a given digital media item may include an image, a video, text, audio, a symbol, a sequence, web content, and/or any type of digital information. In some implementations, digital media component  108  may be configured to obtain information associated with individual ones of the digital media items. In some implementations, information associated with individual ones of the digital media items may comprise information stored as metadata associated with the digital media items. In some implementations, metadata may include one or more of timestamps, physical location where the media item was generated, user-generated information, source-generated information, and/or other information. In some implementations, metadata may be referred to as description, caption, label, tags, price, user reviews, likes, comments, followers, shares, and/or any other metadata. In some implementations, digital media items may be stored on digital, and/or analog storage, may be digitally broadcast, streamed, and/or contained in computer files. In some implementations, digital media items may be obtained from electronic storage  104 , electronic storage  134 , computing platforms  130 , other components of system  100 , and/or other components outside of system  100 . 
     In some implementations, digital media items may be obtained from computing platforms  130 . In some implementations, computing platforms  130  may be associated with one or more of a human user, an automated machine, and/or other source. By way of non-limiting example, a user may provide a digital media item by “uploading” the digital media item, downloading the digital media item, and/or other ways for providing a digital media item to digital media item component  108  from computing platforms  130 . In some implementations, a user may provide one or more digital media items using a user interface  132 . In some implementations, computing platforms  130  may include one or more digital media input devices included with, or coupled to, computing platforms  130 . By way of non-limiting example, a media input device may include one or more of a document scanner, a camera, a microphone, a port configured to be communicatively coupled to a mobile device, and/or other considerations. In some implementations, electronic storage  104  and/or electronic storage  134  may receive a query from a user via computing platforms  130  to provide digital media items to digital media item component  108 . 
       FIG. 2  illustrates an example of a storage system  200 , in accordance with one or more implementations. Storage system  200  may be configured for facilitating the storage of digital media items. Storage system  200  may be configured for supporting queries. In some implementations, storage system  200  may be configured for supporting queries based on information associated with the digital media items. In the example illustrated in  FIG. 2 , digital media items may be stored in electronic storage  210 . In some implementations, digital media items may be stored on remote devices and electronic storage  210  may contain references to the actual location of the digital media items (e.g., file points, urls, and/or other forms of referencing digital media). 
     In some implementations, information included in storage system  200  may be modified by modification  240 . Modification  240  may include one or more of addition of new digital media items, removal of digital media items, modification of information associated with the digital media items stored in electronic storage  210 , and/or other modification of content included within storage system  200  or outside of system  200 . For example, in some implementations, modification  240  may include modification of digital media items stored on remote storage devices and referenced by electronic storage  210 . 
     In some implementations, storage system  200  may include an index  130  for facilitating efficient queries and retrieval of items within electronic storage  210 . The index  130  may be configured to facilitate matching digital media items with corresponding information. In some implementations, index  130  may facilitate direct lookup of content within electronic storage  210  in an efficient way. By way of non-limiting example, indexing may be made efficient by improving one or more of memory usage, time to index, time to query, and/or any other metrics improvements. A query  250  to retrieve digital media items may be made to storage system  200 . A response  260  may be returned. 
     In some implementations, a query  250  may consist of a request for retrieval of information from storage system  200 . In some implementations, a query  250  may be a request to retrieve one or more digital media items matching corresponding information in the request. In some implementations, a query  250  may be made by a user via computing platforms  130  ( FIG. 1 ) and/or other components within or outside system  100  ( FIG. 1 ). In some implementations, query  250  may be made by one or more components within or outside of system  100 . In some implementations, query  250  may include additional operations on the one or more digital items retrieved as a response to query  250 . Additional actions may include updating, inserting, deleting, and/or other actions on the one or more digital media items and/or actions on the information associated with the one or more digital media items retrieved as a response to query  250 . 
     In some implementations, a transformation  220  may be applied to the digital media items to create a representation of the digital media items, such representation may facilitate the efficiency indexing with index  130 . For example, in some implementations, transformation  220  may be configured to process a digital media item with a machine learning system to extract a representation of the digital media item, including relevant information for efficiently indexing the digital media item in index  230 . 
     In some implementations, a response  260  may be retrieved responsive to a query  250 . Response  260  may convey the information requested by query  250 . In some implementations, response  260  may include one or more digital media items requested by query  250 . Response  260  may be retrieved by way of comparison between representations of digital media items in index  230  with a representation included in query  250 . For example, a representation of digital media items may be provided in query  250  as an embedding into a representation space such that comparing embedding of all digital media items in the index  230  can yield a similarity score between the query and the indexed items. Query  250  may ask for the most similar items according to the similarity score, the most dissimilar, and/or any other combination of items to retrieve in response  260 . In some implementations, digital media items representation may be produced from one or more of a neural network, other machine learning systems, and/or other computing platforms. 
     Returning to  FIG. 1 , concept component  110  may be configured to obtain an indication conveying a concept to be learned from the one or more digital media items. A concept may refer to one or more of information, an idea, a notion, and/or any understanding that can be learned from one or more digital media items. In some implementations, a concept may refer to a sentiment, an adjective, a verb, a noun, an abstract notion, a concrete notion, and/or any other information that can be learned. For example, the concept of a “dog” may be learned from one or more photos that may contain a dog in them, from one or more sounds of dogs, from one or more descriptions of dogs, and/or from other information related to dogs. By way of non-limiting example, the concept “happy” may be learned from one or more images depicting happy people, from one or more sounds, from one or more audios of laughter, from one or more audios of music, from one or more audios of songs, from one or more texts, and/or other information related to “happy”. An indication conveying the concept to be learned may be in the form of one or more of text, sound, voice, picture, and/or other forms for conveying the concept to be learned. The indication conveying the concept to be learned may be obtained from computing platform(s)  130 , a user, and/or other components within or outside system  100 . 
     Feedback component  112  may be configured to receive feedback associated with individual ones of the one or more digital media items. Feedback received by feedback component  112  may be received from one or more components within or outside of system  100 . In some implementations, the feedback may be received from computing platforms  130 . Computing platforms  130  may be configured to provide feedback associated with individual ones of the one or more digital media items to feedback component  112  based on a user input. In some implementations, receiving feedback for individual ones of the digital media items may include obtaining confirmation of information associated with the individual ones of the digital media items. In some implementations receiving feedback for individual ones of the digital media items may include obtaining confirmation of previous predictions related to the concept to be learned. 
     In some implementations, feedback provided to feedback component  112  may be based on selection of one or more positive examples of the concept to be learned from the one or more digital media items such that a given positive example may be a digital media item related to the concept to be learned. The selection may be obtained from a user via user interface  132  and/or one or more components within or outside system  100 . In some implementations, feedback provided to feedback component  112  may be based on selection of one or more negative examples of the concept to be learned from the one or more digital media items such that a given negative example may be a digital media item not related to the concept to be learned. By way of non-limiting example, positive examples of the concept of a “Labrador Retriever” may include images of a Labrador retriever. Negative examples of the concept of a “Labrador Retriever” may include images of cars, trees, mountains, Rottweilers, Poodles, and/or other images which do not include a Labrador retriever. In some implementations, feedback provided to feedback component  112  may be based on selection of one or more positive examples and one or more negative examples of the concept to be learned. 
     Machine learning component  114  may be configured to obtain individual machine learning representations for the individual ones of the one or more digital media items. In some implementations, the machine learning representations may be neural network representations for the individual ones of the one or more digital media items. A neural network is typically organized in layers made up of a number of interconnected ‘nodes’ which contain an activation function. Digital media items may be presented to the network via an ‘input layer’, which communicates to one or more ‘hidden layers’ where the actual processing is done via a system of weighted ‘connections’. The hidden layers then link to an ‘output layer’ where a prediction is output. A given neural network representation may include one or more neural network layers. By way of non-limiting example, a convolutional neural network may be used for processing images. A convolutional neural network may be comprised of one or more convolutional neural network layers to learn weights shared over an image, one of more pooling layers to make the neural network more invariant, one or more non-linear activation function layers to make the network more robust and/or other type of neural network layers while taking in the image as input and outputting a vector representation encoding high level information extracted from the image. By way of non-limiting example, for text, audio, and/or time series processing a recurrent neural network may be used to encode time series information as a vector output using weighted connections through time to process the time sequence of inputs. 
     Training component  116  may be configured to determine one or more trained concepts. The trained concept may be used for retrieving digital media items relevant to the concept to be learned. In some implementations, training component  116  may be configured to determine a trained concept based on one or more of the feedback associated with one or more digital media items, the one or more digital media items, and/or the individual representations of the one or more digital media items. In some implementations, training component  116  may be configured to determine additional neural network layers based on the feedback associated with one or more digital media items, and/or the neural network representations of the one or more digital media items. 
       FIG. 3  illustrates a system  300  configured for learning new concepts based on information and feedback from a computing platform  310 , in accordance with one or more implementations. Digital media items included in electronic storage system  330  may be modified by modification  340 . Modification  340  may include one or more of addition of new digital media items, removal of digital media items, modification of information associated with the digital media items stored in electronic storage  330 , and/or other modification to content stored in electronic storage system  330 . Digital media items may be retrieved from electronic storage system  330  and presented to computing platform  310 . In some implementations, computing platform  310  may include a user interface. 
     Feedback  320  associated with one or more digital media items may be received from computing platform  310 . In some implementations, feedback  320  may be conveyed in the form of selection of digital media items, selection of labels, selection of text, association of digital media items with information relevant to the concept to learned, and/or other forms of feedback. For example, a concept to be learned is “dog”. Feedback  320  associated with a set of images may be in the form of a set of selected images showing a dog, a set of selected images not showing a dog. In some implementations feedback  320  may include confirmation of previous predictions by system  300 , labelling digital media items as belonging to positive examples of the concept to be learned, labelling digital media items as belonging to negative examples of the concept to be learned, and/or other information relevant to the concept to be learned. In some implementations, feedback  320  may be based on user interactions with system  300 . Examples of interactions with system  300  may include one or more of search history, search click through rate, liking content, sharing content, commenting on content, subscribing to content, and/or other interactions with system  300 . 
     In some implementations, training component  350  may be configured to retrieve digital media items, from electronic storage  330 , for which feedback  320  is provided. In some implementations, digital media items may be retrieved from electronic storage  330  using a query. In some implementations, training component  350  may include a machine learning system, a computing platform, and/or a human operator(s). Training component  350  may be configured to learn from feedback  320  and digital media items retrieved from electronic storage  330 . For example, training component  350  may improve predictions of the machine learning system based on the feedback  320  associated with the digital media items. 
     Training component  350  may be configured to determine a trained concept  360 . In some implementations, trained concept  360  may represent the parameters of a machine learning method used by the training component  350  to learn from feedback  350 . By way of non-limiting example, digital media items in electronic storage  330  may be a set of images, feedback  320  may be labels of images containing birds and labels of images not containing birds. The training component may learn from the labels provided by feedback  320 . The labels provided by feedback  320  may be the parameters defining a bird classifier determined by the training component  350 . 
     Returning to  FIG. 1 , electronic storage  104  may receive queries for digital media items related to the concept being learned. The trained concept obtained by training component  116  may be used to obtain results of the queries. In some implementations, additional feedback associated with the results of the queries may be received by training component  116 . The feedback may be based on selection of one or more positive examples of the concept being learned from results of the queries, and/or selection of one or more negative examples of the concept being learned from the results of the queries. In some implementations, the trained concept may be adjusted based on the additional feedback. In some implementations, machine learning component  114  may be configured to obtain neural network representations of the results of the queries. The trained concept may be adjusted based on the additional feedback and/or the neural network representation of the results of the queries. By way of non-limiting example, for a trained concept of a “dog”, additional feedback may include more positive examples of “dogs” (true positives) retrieved by querying for similar items to the trained concept, negative examples that were not “dogs” (false positives) when querying for similar items to the trained concept, negative examples that are not “dogs” (true negatives) when querying for dissimilar items to the trained concept, and/or positive examples of “dogs” (false negatives) when querying for dissimilar items to the trained concept. 
       FIG. 4  illustrates a system configured for learning new trained concepts, in accordance with one or more implementations. In some implementations, digital media items may be obtained by computing platform  410 . Feedback  420  associated with individual ones of the digital media items may be generated. Feedback  420  and digital media items associated with feedback  420  may be provided to training component  430 . Training component may be configured to learn from feedback  420  and digital media associated with feedback  420 . A trained concept  440  may be determined by training component  430 . In some implementations, trained concept  440  may be transformed using a model transformation  470 . The transformation  470  may provide adjustments to the trained concept  440  to make it suitable in a query  450  on electronic storage  408 . Query  450  may be used to retrieve more relevant digital media items to the concept to be learned. The results of query  450  may be returned in a response  460 . The iterative procedure shown in  FIG. 4  may be used to refine the trained concept  440  by presenting digital media items that were retrieved using the previously trained concept  440 , to the computing platform  410  for additional feedback generation. 
     By way of non-limiting example, “dog” is the concept to be learned by training component  430 . One or more images may be retrieved from electronic storage  408 . Images representing a “dog” are provided as positive feedback  420  and images not representing a “dog” are provided as negative feedback  420 . The training component  430  may leverage a machine learning framework configured to learn from the feedback  420  and where the trained concept  440  may be the machine learning representation of the “dog” concept. Electronic storage  408  may maintain a machine learning representation for individual one of the images stored in electronic storage  408 . The training component  430  may be provided with the machine learning representation for the images for which a feedback  420  has been generated. The machine learning representations of the images and the feedback  420  may be used by training component  430  to learn additional machine learning representations to output as a trained concept  440 . 
     In some implementations, computing platform  410  may obtain more information to iterate the operations described above. The trained concept  440  may be transformed using transformation  470  to make it suitable to a query  450  on electronic storage  408 . Query  450  may be used to retrieve more relevant digital media items returned in a response  460 . The digital media items retrieved may then be used to provide positive, and/or negative feedback  420  to further teach training component  430 . 
     Returning to  FIG. 1 , processor(s)  106  and/or  136  may be configured to provide information processing capabilities in system  100  (e.g., in server(s)  102  and/or computing platform(s)  130 ). As such, processor(s)  106  and/or processor  136  may include one or more of a digital processor, an analog processor, a digital circuit designed to process information, an analog circuit designed to process information, a state machine, and/or other mechanisms for electronically processing information. Although processor(s)  106  and/or processor  136  are shown in  FIG. 1  as a single entity, this is for illustrative purposes only. In some implementations, processor(s)  106  and/or processor  136  may include a plurality of processing units. These processing units may be physically located within the same apparatus (e.g., in server(s)  102  and/or computing platform(s)  130 ), or processor(s)  106  and/or  136  may represent processing functionality of a plurality of apparatuses operating in coordination (e.g., in server(s)  102  and/or computing platform(s)  130 ). 
     Electronic storage  104 , electronic storage  134 , and/or electronic storage  210  ( FIG. 2 ) may comprise electronic storage media that electronically stores information. The electronic storage media of electronic storage  104 , electronic storage  134 , and or electronic storage  210  ( FIG. 2 ) may include one or both of system storage that is provided integrally (i.e., substantially non-removable) with server(s)  102  and/or computing platform  130 , and/or may contain removable storage that is removably connectable to server(s)  102  and/or computing platform  130  via, for example, a port or a drive. A port may include a USB port, a firewire port, and/or other port. A drive may include a disk drive and/or other drive. Electronic storage  104 , electronic storage  134 , and or electronic storage  210  ( FIG. 2 ) may include one or more of optically readable storage media (e.g., optical disks, etc.), magnetically readable storage media (e.g., magnetic tape, magnetic hard drive, floppy drive, etc.), electrical charge-based storage media (e.g., EEPROM, RAM, etc.), solid-state storage media (e.g., flash drive, etc.), and/or other electronically readable storage media. The electronic storage  210  may include one or more virtual storage resources (e.g., cloud storage, a virtual private network, and/or other virtual storage resources). Electronic storage  104 , electronic storage  134 , and/or electronic storage  210  ( FIG. 2 ) may store digital media items, software algorithms, information determined by processor(s)  106  and/or  136 , information received from server  102 , and/or computing platform  130 , and/or other information that enables server  102  and/or computing platform  130  to function as described herein. 
     Computing platform(s)  130  may include user interface  132 , electronic storage  134 , one or more processors  136 , and/or other components. User interface  132  may be configured to provide an interface between system  100  and a user through which the user may provide information to and receive information from system  100 . This enables information, results, and/or instructions and any other communicable items, collectively referred to as “information”, to be communicated between the user and one or more components of system  100 . Examples of interface devices suitable for inclusion in user interface  132  include one or more of a keypad, buttons, switches, a keyboard, knobs, levers, a display screen, a touch screen, speakers, a microphone, an indicator light, an audible alarm, a printer, and/or other devices. In some implementations, user interface  132  may include a plurality of separate interfaces, including an interface that may be provided in server(s)  102 , and a separate interface provided to view and/or manage stored information that has been retrieved from server(s)  102  (e.g., provided by a computer configured to receive information from server(s)  102  and other components of system  100 ). 
     The external resources  105  may include sources of information, hosts and/or providers of information outside of system  100 , external entities participating with system  100 , and/or other resources. In some implementations, some or all of the functionality attributed herein to external resources  105  may be provided by resources included in system  100  (e.g., in server(s)  102 ). 
     The network  150  may include the Internet and/or other networks, Intranets, near field communication, frequency (RF) link, Bluetooth®, Wi-Fi, and/or any type(s) of wired or wireless network(s). It will be appreciated that this is not intended to be limiting and that the scope of this disclosure includes implementations in which the server(s)  102 , the computing platform(s)  130 , and/or the external resource(s)  140 , are operatively linked via some other communication media. 
     It should be appreciated that, although components  108 ,  110 ,  112 ,  114  and/or  116  are illustrated in  FIG. 1  as being co-located within a single component, in implementations in which processor  106  [and/or  136 ] is configured by machine-readable instructions  107  to execute multiple components, one or more of components  108 ,  110 ,  112 , and/or  114  may be located remotely from the other components. The description of the functionality provided by the different components  108 ,  110 ,  112 ,  114  and/or  116  described above is for illustrative purposes and is not intended to be limiting, as any of components  108 ,  110 ,  112 ,  114  and/or  116  may provide more or less functionality than is described. For example, one or more of components  108 ,  110 ,  112 ,  114  and/or  116  may be eliminated, and some or all of its functionality may be provided by other ones of components  108 ,  110 ,  112 ,  114 ,  116 , and/or other components. As another example, processor  104  may be configured by machine-readable instructions  107  to execute one or more additional components that may perform some or all of the functionality attributed below to one of components  108 ,  110 ,  112 ,  114  and/or  116 . 
       FIG. 5  illustrates a method  500  for learning new trained concepts used to retrieve content relevant to the concepts learned. The operations of method  500  presented below are intended to be illustrative. In some embodiments, method  500  may be accomplished with one or more additional operations not described, and/or without one or more of the operations discussed. Additionally, the order in which the operations of method  500  are illustrated in  FIG. 5  and described below is not intended to be limiting. 
     In some embodiments, method  500  may be implemented in one or more processing devices (e.g., a digital processor, an analog processor, a digital circuit designed to process information, an analog circuit designed to process information, a state machine, a functionally limited processing device, and/or other mechanisms for electronically processing information). The one or more processing devices may include one or more devices executing some or all of the operations of method  500  in response to instructions stored electronically on an electronic storage medium. The one or more processing devices may include one or more devices configured through hardware, firmware, and/or software to be specifically designed for execution of one or more of the operations of method  500 . 
     At an operation  502 , one or more digital media items may be obtained. In some implementations, operation  502  may be performed by a digital media component the same as or similar to digital media component  108  (shown in  FIG. 1  and described herein). 
     At an operation  504 , an indication conveying a concept to be learned from the one or more digital media items may be obtained. In some implementations, operation  504  may be performed by a concept component the same as or similar to the concept component  110  (shown in  FIG. 1  and described herein). 
     At an operation  506 , a feedback associated with individual ones of the one or more digital media items may be received. The feedback may be based on selection of one or more positive examples of the concept to be learned from the one or more digital media items, selection of one or more negative examples of the concept to be learned from the one or more digital media items, and/or a combination thereof. In some implementations, a given positive example may be a digital media item comprising the concept to be learned. In some implementations, a given negative example may be a digital media item lacking the concept to be learned. In some implementations, operation  506  may be performed by a feedback component the same as or similar to the feedback component  112  (shown in  FIG. 1  and described herein). 
     At operation  508 , individual machine learning representations for the individual ones of the one or more digital media items are obtained. In some implementations, individual neural network representations for the individual ones of the one or more digital media items are obtained. In some implementations, a given neural network representation may include one or more neural network layers. In some implementations, operation  508  may be performed by a machine learning component the same as or similar to the machine learning component  114  (shown in  FIG. 1  and described herein). 
     At operation  510 , a trained concept may be obtained. In some implementations, the trained concept may be based on the feedback, the machine learning representations of the one or more digital media items, and/or a combination thereof. In some implementations, operation  510  may be performed by a training component the same as or similar to the training component  116  (shown in  FIG. 1  and described herein). In some implementations, the trained concept may be used for retrieving digital media items relevant to the concept to be learned. The retrieved digital media items may be used to reiterate operations  502 - 510  described above. 
     Although the present technology has been described in detail for the purpose of illustration based on what is currently considered to be the most practical and preferred implementations, it is to be understood that such detail is solely for that purpose and that the technology is not limited to the disclosed implementations, but, on the contrary, is intended to cover modifications and equivalent arrangements that are within the spirit and scope of the appended claims. For example, it is to be understood that the present technology contemplates that, to the extent possible, one or more features of any implementation can be combined with one or more features of any other implementation.