Abstract: A system for training a neural network includes a neural network configured to receive a training input in an image space and produce an enhanced image. The system further includes an error signal generator configured to compare the enhanced image to a ground truth and generate an error signal that is communicated back to the neural network to train the neural network. Additionally, the system includes a neural input enhancer configured to modify the training input in response to receiving at least one of an output from the neural network or the error signal. Modifying the training input improves one of an efficiency or a training result of the neural network beyond the communication of the error signal to only the neural network.

Abstract: The disclosure generally relates to the artificial intelligence (AI) automatic methods, computer program product, and systems and methodology for dietary and medical treatment planning, food waste estimation, analyzing three-dimensional food image construction, measurement, nutrient estimation, nutritional assessment, evaluation, prediction and management. More particularly, the embodiments described herein relate to utilizing an AI-based algorithm that can automatically, detect food items from images acquired by cameras for dietary assessment, dietary planning, and for estimating food waste. In one aspect, the method may include food calorie estimation techniques using machine learning and computer vision techniques for dietary assessment. In another aspect, the tools may apply to personalized nutrition. The method may also include the automation of nutrition planning.

Abstract: A method comprising: obtaining a plurality of data sets, each of the plurality of data sets being generated, at least in part, by using by a different one of a plurality of sensors, each of a plurality of sensors including a wearable sensor that is worn by a user or a sensor that is positioned at a same location as the user; combining the plurality of data sets to produce a fused data set; processing the fused data set to identify at least one of a performance of the user in completing a task, a cognitive load of the user, and/or one or more objects that are positioned at the same location as the user; and outputting an indication of a state of the user based on an outcome of the processing.