Abstract: Systems and methods are disclosed related to synthetic bracketing for exposure correction. A deep learning based method and system produces a set of differently exposed images from a single input image. The images in the set may be combined to produce an output image with improved global and local exposure compared with the input image. An image encoder applies learned parameters to each input image to generate a set of image features including local exposure estimates for each of two or more regions of the input image and a low resolution latent representation of the input image. A decoder receives the local exposure estimates, the latent representation, and target enhancements that are processed to generate synthesized transformations. When applied to the input image, the synthesized transformations produce the set of transformed images. Each transformed image is a version of the input image synthesized to correspond to a respective target enhancement.

Abstract: Provided is a method, computer program product, and system for predicting image sharing decisions using machine learning. A processor may receive a set of annotated images and an associated text input from each user of a plurality of users. The processor may train, using the set of annotated images and the associated text input from each user, a neural network model to output an image sharing decision that is specific to a user.

Abstract: Provided is a method, computer program product, and system for predicting image sharing decisions using machine learning. A processor may receive a set of annotated images and an associated text input from each user of a plurality of users. The processor may train, using the set of annotated images and the associated text input from each user, a neural network model to output an image sharing decision that is specific to a user.

Abstract: A computer-implemented method generates a hamming code based target label for each class of a dataset in which hamming distance between the target labels in the dataset is maximized and trains a convolutional neural network with the hamming codes based target label to thereby produce a trained AI model. The confusability between classes of the dataset is determined using a confusion matrix. The hamming distances of classes of the dataset that are determined to be more confusable are set to higher values than the hamming distances of classes of the dataset that are determined to be less confusable.

Abstract: An embodiment includes identifying an initial plurality of sets of hyperparameter values at which to evaluate an objective function that relates hyperparameter values to performance values of a neural network. The embodiment also executes training processes on the neural network with the hyperparameters set to the each of the initial sets of hyperparameter values such that the training process provides an initial set of the performance values for the objective function. The embodiment also generates an approximation of the objective function using splines at selected performance values. The embodiment approximates a point at which the approximation of the objective function reaches a maximum value, then determines an updated set of hyperparameter values associated with the maximum value. The embodiment then executes a runtime process using the neural network with the hyperparameters set to the updated set of hyperparameter values.

Abstract: A computer-implemented method is provided. The embodiments include evaluating, by one or more processors, a specimen chart relative to a chart erratum model that has features mapped to an optimum state for a first chart type. The method also includes generating a first risk score for a first sample feature of the specimen chart. The first risk score may include a delta from the optimum state. The method also includes refactoring the specimen chart to mitigate the first risk score of the first sample feature.

Abstract: A computer-implemented method generates a hamming code based target label for each class of a dataset in which hamming distance between the target labels in the dataset is maximized and trains a convolutional neural network with the hamming codes based target label to thereby produce a trained AI model. The confusability between classes of the dataset is determined using a confusion matrix. The hamming distances of classes of the dataset that are determined to be more confusable are set to higher values than the hamming distances of classes of the dataset that are determined to be less confusable.