Image-to-image regression is an important learning task, used frequently in biological imaging. Current algorithms, however, do not generally offer statistical guarantees that protect against a model’s mistakes and hallucinations. To address this, we develop uncertainty quantification techniques with rigorous statistical guarantees for image-to-image regression problems. In particular, we show how to derive uncertainty intervals around each pixel that are guaranteed to contain the true value with a user-specified confidence probability. Our methods work in conjunction with any base machine learning model, such as a neural network, and endow it with formal mathematical guarantees{—}regardless of the true unknown data distribution or choice of model. Furthermore, they are simple to implement and computationally inexpensive. We evaluate our procedure on three image-to-image regression tasks: quantitative phase microscopy, accelerated magnetic resonance imaging, and super-resolution transmission electron microscopy of a Drosophila melanogaster brain.

Traffic signs recognition (TSR) is a crucial sub-domain of computer vision, particularly relevant to autonomous vehicles and autonomous driver-assistance systems (ADAS). TSR systems can further augment efforts in many other applications, such as highway asset maintenance and management. Despite the relative success of detectors with hand-crafted features in Europe, most, if not all, non-deep-learning based systems are not scalable to accurately recognize a large subset of traffic signs in the United States. The recent works in the domain of object detection using machine learning have shown the necessity of deep neural networks (DNNs) in TSR, whereby a DNN can learn features automatically without hand-crafting them. Due to the lack of datasets in the U.S. and the inefficient use of traditional methods for traffic sign recognition (TSR) in the U.S., we created the Automotive Repository of Traffic Signs (ARTS), a new dataset for traffic signs recognition in the U.S. ARTS covers a wide range of sign-types, including Regulatory, Guide, Warning, and Temporary signs as defined in the Manual on Uniform Traffic Control Devices (MUTCD). It also features geospatial data to localize signs using their GPS coordinates. Benchmarks are presented to assess the performance of state-of-the-art deep learning based detectors.