In one embodiment an image is processed using a deep-learning model to obtain one or more first predictions. Each first prediction is a likelihood that a respective first concept in a set of first concepts is associated with the image. A feature vector is retrieved for the image. The feature vector is output from a processing layer of the deep-learning model for the image. The feature vector for the image is processed using a first linear model, where the first linear model is trained to detect one or more second concepts. One or more second predictions are obtained from the first linear model. Each second prediction is a likelihood that a respective second concept of the one or more second concepts is associated with the image.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method comprising: processing, by one or more computing devices, an image, using a deep-learning model, to obtain one or more first predictions, each first prediction being a likelihood that a respective first concept in a set of first concepts is associated with the image; retrieving, by one or more computing devices, a feature vector for the image, wherein the feature vector comprises output from a processing layer of the deep-learning model for the image; processing, by one or more computing devices, the feature vector for the image using a first linear model, wherein the first linear model is trained to detect one or more second concepts; and obtaining, by one or more computing devices, one or more second predictions from the first linear model, each second prediction being a likelihood that a respective second concept of the one or more second concepts is associated with the image. 2. The method of claim 1, wherein the processing layer from which the feature vector is outputted is a layer beneath an output layer of the deep-learning model. 3. The method of claim 1, wherein the first predictions and the second predictions are obtained at substantially simultaneously. 4. The method of claim 1, wherein the set of first concepts does not comprise the one or more second concepts. 5. The method of claim 1, wherein the deep-learning model is a neural network. 6. The method of claim 1, wherein the one or more second predictions obtained from the linear model are approximations of learned predictions. 7. The method of claim 1, further comprising: processing, by one or more computing devices, the feature vector for the image using a second linear model, wherein the linear model is trained to detect one or more third concepts; and obtaining, by one or more computing devices, one or more third predictions from the linear model, each third prediction being a likelihood that a respective third concept of the one or more third concepts is associated with the image. 8. The method of claim 7, wherein the feature vector for the image is processed concurrently by the first linear model and the second linear model. 9. The method of claim 7, wherein the set of first concepts does not comprise the one or more third concepts. 10. One or more computer-readable non-transitory storage media embodying software that is operable when executed to: process an image, using a deep-learning model, to obtain one or more first predictions, each first prediction being a likelihood that a respective first concept in a set of first concepts is associated with the image; retrieve a feature vector for the image, wherein the feature vector comprises output from a processing layer of the deep-learning model for the image; process the feature vector for the image using a first linear model, wherein the first linear model is trained to detect one or more second concepts; and obtain one or more second predictions from the first linear model, each second prediction being a likelihood that a respective second concept of the one or more second concepts is associated with the image. 11. The media of claim 10, wherein the processing layer from which the feature vector is outputted is a layer beneath an output layer of the deep-learning model. 12. The method of claim 10, wherein the first predictions and the second predictions are obtained substantially simultaneously. 13. The media of claim 10, wherein the set of first concepts does not comprise the one or more second concepts. 14. The media of claim 10, wherein the deep-learning model is a neural network. 15. 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: process an image, using a deep-learning model, to obtain one or more first predictions, each first prediction being a likelihood that a respective first concept in a set of first concepts is associated with the image; retrieve a feature vector for the image, wherein the feature vector comprises output from a processing layer of the deep-learning model for the image; process the feature vector for the image using a first linear model, wherein the first linear model is trained to detect one or more second concepts; and obtain one or more second predictions from the first linear model, each second prediction being a likelihood that a respective second concept of the one or more second concepts is associated with the image. 16. The system of claim 15, wherein the processing layer from which the feature vector is outputted is a layer beneath an output layer of the deep-learning model. 17. The system of claim 15, wherein the first predictions and the second predictions are obtained substantially simultaneously. 18. The system of claim 15, wherein the set of first concepts does not comprise the one or more second concepts. 19. The system of claim 15, wherein the deep-learning model is a neural network.