Abstract: An image quality scorer machine accesses a candidate image to be analyzed for visual quality. The image quality scorer machine generates a visual quality score of the candidate image by first generating a prediction of a similarity score for the candidate image. The predicted similarly score of the candidate image may be generated by a process including inputting the candidate image into a neural network that has been trained to detect a set of image features in the candidate image and then to generate a corresponding predicted similarity score based on degrees to which the image features in the set are present in the candidate image. The image quality scorer machine derives the visual quality score based on the predicted similarity score outputted by the neural network. Accordingly, the image quality score machine may provide or store the generated visual quality score of candidate image for subsequent usage.

Abstract: An image quality scorer machine accesses a candidate image to be analyzed for visual quality. The image quality scorer machine generates a visual quality score of the candidate image by first generating a prediction of a similarity score for the candidate image. The predicted similarly score of the candidate image may be generated by a process including inputting the candidate image into a neural network that has been trained to detect a set of image features in the candidate image and then to generate a corresponding predicted similarity score based on degrees to which the image features in the set are present in the candidate image. The image quality scorer machine derives the visual quality score based on the predicted similarity score outputted by the neural network. Accordingly, the image quality score machine may provide or store the generated visual quality score of candidate image for subsequent usage.

Abstract: Systems and methods for training models and using the models to detect events are provided. A networked system assembles one or more triplets using sensor data accessed from a plurality of user devices, the assembling including applying a weak label. The networked system autoencodes the one or more triplets based on a covariate to generate a disentangled embedding. A model is trained using the disentangled embedding, whereby the model is used at runtime to detect whether an event associated with the model is present. In particular, runtime sensor data from the real world is autoencoded to generate a runtime embedding, whereby the runtime sensor data comprising sensor data from at least one of a device of a user. The runtime embedding is comparted to one or more embeddings of the model, whereby a similarity in the comparing indicates the event associated with the model occurring in the real world.