Abstract: An unsupervised real to virtual domain unification model for highway driving, or DU-drive, employs a conditional generative adversarial network to transform driving images in a real domain to their canonical representations in the virtual domain, from which vehicle control commands are predicted. In the case where there are multiple real datasets, a real-to-virtual generator may be independently trained for each real domain and a global predictor could be trained with data from multiple real domains. Qualitative experiment results show this model can effectively transform real images to the virtual domain while only keeping the minimal sufficient information, and quantitative results verify that such canonical representation can eliminate domain shift and boost the performance of control command prediction task.

Abstract: Organ segmentation in chest X-rays using convolutional neural networks is disclosed. One embodiment provides a method to train a convolutional segmentation network with chest X-ray images to generate pixel-level predictions of target classes. Another embodiment will also train a critic network with an input mask, wherein the input mask is one of a segmentation network mask and a ground truth annotation, and outputting a probability that the input mask is the ground truth annotation instead of the prediction by the segmentation network, and to provide the probability output by the critic network to the segmentation network to guide the segmentation network to generate masks more consistent with learned higher-order structures.

Abstract: A system for assigning concepts to a medical image includes a visual feature module and a tagging module. The visual feature module is configured to obtain an image feature vector from the medical image. The tagging module is configured to apply a machine-learned algorithm to the image feature vector to assign a set of concepts to the image. The system may also include a text report generator that is configured to generate a written report describing the medical image based on the set of concepts assigned to the medical image.

Abstract: Accordingly, a data engineering system for machine learning at scale is disclosed. In one embodiment, the data engineering system includes an ingest processing module having a schema update submodule and a feature statistics update submodule, wherein the schema update submodule is configured to discover new features and add them to a schema, and wherein the feature statistics update submodule collects statistics for each feature to be used in an online transformation, a record store to store data from a data source, and a transformation module, to receive a low dimensional data instance from the record store and to receive the schema and feature statistics from the ingest processing module, and to transform the low dimensional data instance into a high dimensional representation.

Abstract: A computer system implemented a method for elastic resource management for executing a machine learning (ML) program.