In one embodiment, a system retrieves a first feature vector for an image. The image is inputted into a first deep-learning model, which is a first-version model, and the first feature vector may be output from a processing layer of the first deep-learning model for the image. The first feature vector using a feature-vector conversion model to obtain a second feature vector for the image. The feature-vector conversion model is trained to convert first-version feature vectors to second-version feature vectors. The second feature vector is associated with a second deep-learning model, and the second deep-learning model is a second-version model. The second-version model is an updated version of the first-version model. A plurality of predictions for the image may be generated using the second feature vector and the second deep-learning model.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method comprising: retrieving, by one or more computing devices, a first feature vector for an image, wherein the image is inputted into a first deep-learning model, the first deep-learning model being of a first version, and wherein the first feature vector comprises output from a processing layer of the first deep-learning model for the image; processing, by one or more computing devices, the first feature vector using a feature-vector conversion model to obtain a second feature vector for the image, the feature-vector conversion model being trained to convert first-version feature vectors to second-version feature vectors, wherein the second feature vector is associated with a second deep-learning model, the second deep-learning model being of a second version, the second version being an updated version of the first version; and generating, by one or more computing devices, a plurality of predictions for a for the image using the second feature vector and the second deep-learning model. 2. The method of claim 1, wherein the processing layer from which the first feature vector is outputted is a layer beneath an output layer of the first deep-learning model. 3. The method of claim 1, further comprising: determining that the first feature vector is a first-version feature vector, the first version being an earlier version than the second version; and selecting the first feature-vector conversion model out of a plurality of feature-vector conversion models to convert the first feature vector to the second feature vector based on the first feature vector being a first-version feature vector, wherein each of the plurality of feature-vector conversion models is trained to convert a respective particular version feature model to the second-version feature vector. 4. The method of claim 3, further comprising adding a feature-vector conversion layer to the second deep-learning model, wherein the feature-vector conversion layer comprises the plurality of feature-vector conversion models, and wherein the feature-vector conversion layer takes as input the first feature vector and outputs the second feature vector. 5. The method of claim 1, wherein the second feature vector comprises an approximation of a particular second-version feature vector obtainable by processing the image using the second deep-learning model. 6. The method of claim 1, wherein processing power used by the feature-vector conversion model to convert the first feature vector to the second feature vector for the image is less than processing power necessary to process the image using the second deep-learning model. 7. The method of claim 1, wherein each of the plurality of predictions comprises a likelihood that a respective concept is associated with the image. 8. The method of claim 1, wherein retrieving the first feature vector comprises accessing the image, the first feature vector being stored in association with the image. 9. The method of claim 1, wherein the feature-vector conversion model comprises a neural network. 10. The method of claim 9, wherein the feature-vector conversion model comprises fully-connected layers. 11. The method of claim 1, wherein the feature-vector conversion model is trained to minimize error in converting first-version feature vectors to second-version feature vectors. 12. One or more computer-readable non-transitory storage media embodying software that is operable when executed to: retrieve a first feature vector for an image, wherein the image is inputted into a first deep-learning model, the first deep-learning model being of a first version, and wherein the first feature vector comprises output from a processing layer of the first deep-learning model for the image; process the first feature vector using a feature-vector conversion model to obtain a second feature vector for the image, the feature-vector conversion model being trained to convert first-version feature vectors to second-version feature vectors, wherein the second feature vector is associated with a second deep-learning model, the second deep-learning model being of a second version, the second version being an updated version of the first version; and generate a plurality of predictions for the image using the second feature vector and the second deep-learning model. 13. The media of claim 12, wherein the processing layer from which the first feature vector is outputted is a layer beneath an output layer of the first deep-learning model. 14. The media of claim 12, wherein the software is further operable when executed to: determine that the first feature vector is a first-version feature vector, the first version being an earlier version than the second version; and select the first feature-vector conversion model out of a plurality of feature-vector conversion models to convert the first feature vector to the second feature vector based on the first feature vector being a first-version feature vector, wherein each of the plurality of feature-vector conversion models is trained to convert a respective particular version feature model to the second-version feature vector. 15. The media of claim 14, wherein the software is further operable when executed to add a feature-vector conversion layer to the second deep-learning model, wherein the feature-vector conversion layer comprises the plurality of feature-vector conversion models, and wherein the feature-vector conversion layer takes as input the first feature vector and outputs the second feature vector. 16. A system comprising: one or more processors; and a memory coupled to the processors comprising instructions executable by the processors, the processors operable when executing the instructions to: retrieve a first feature vector for an image, wherein the image is inputted into a first deep-learning model, the first deep-learning model being of a first version, and wherein the first feature vector comprises output from a processing layer of the first deep-learning model for the image; process the first feature vector using a feature-vector conversion model to obtain a second feature vector for the image, the feature-vector conversion model being trained to convert first-version feature vectors to second-version feature vectors, wherein the second feature vector is associated with a second deep-learning model, the second deep-learning model being of a second version, the second version being an updated version of the first version; and generate a plurality of predictions for the image using the second feature vector and the second deep-learning model. 17. The system of claim 16, wherein the processing layer from which the first feature vector is outputted is a layer beneath an output layer of the first deep-learning model. 18. The system of claim 16, wherein the software is further operable when executed to: determine that the first feature vector is a first-version feature vector, the first version being an earlier version than the second version; and select the first feature-vector conversion model out of a plurality of feature-vector conversion models to convert the first feature vector to the second feature vector based on the first feature vector being a first-version feature vector, wherein each of the plurality of feature-vector conversion models is trained to convert a respective particular version feature model to the second-version feature vector. 19. The system of claim 18, wherein the software is further operable when executed to add a feature-vector conversion layer to the second deep-learning model, wherein the feature-vector conversion layer comprises the plurality of feature-vector conversion models, and wherein the feature-vector conversion layer takes as input the first feature vector and outputs the second feature vector.