A system and method estimate a future path ahead of a current location of a vehicle. The system includes at least one processor programmed to: obtain an image of an environment ahead of a current arbitrary location of a vehicle navigating a road; obtain a trained system that was trained to estimate a future path on a first plurality of images of environments ahead of vehicles navigating roads; apply the trained system to the image of the environment ahead of the current arbitrary location of the vehicle; and provide, based on the application of the trained system to the image, an estimated future path of the vehicle ahead of the current arbitrary location.

An automatic target recognizer system including: a database that stores target recognition data including multiple reference features associated with each of multiple reference targets; a pre-selector that selects a portion of the target recognition data based on a reference gating feature of the multiple reference features; a preprocessor that processes an image received from an image acquisition system which is associated with an acquired target and determines an acquired gating feature of the acquired target; a feature extractor and processor that discriminates the acquired gating feature with the reference gating feature and, if there is a match, extracts multiple segments of the image and detects the presence, absence, probability or likelihood of one of multiple features of each of the multiple reference targets; a classifier that generates a classification decision report based on a determined classification of the acquired target; and a user interface that displays the classification decision report.

A decision support system and method, which receives user inputs comprising: at least one user criterion, and at least one user input tuning parameter representing user tradeoff preferences for producing an output; and selectively produces an output of tagged data from a clustered database in dependence on the at least one user criterion, the at least one user input tuning parameter, and a distance function; receives at least one reference-user input parameter representing the at least one reference-user's analysis of the tagged data and the corresponding user inputs, to adapt the distance function in accordance with the reference-user inputs as a feedback signal; and clusters the database in dependence on at least the distance function, wherein the reference-user acts to optimize the distance function based on the user inputs and the output, and on at least one reference-user inference.

Techniques for content generation are provided. A plurality of discriminative terms is determined based at least in part on a first plurality of documents that are related to a first concept, and a plurality of positive exemplars and a plurality of negative exemplars are identified using the plurality of discriminative terms. A first machine learning (ML) model is trained to classify images into concepts, based on the plurality of positive exemplars and the plurality of negative exemplars. A second concept related to the first concept is then determined, based on the first ML model. A second ML model is trained to generate images based on the second concept, and a first image is generated using the second ML model. The first image is then refined using a style transfer ML model that was trained using a plurality of style images.

In some embodiments, a computer-implemented method is disclosed. The method comprises obtaining a first digital model for classifying an image into a class of a first set of classes corresponding to a first plurality of plant diseases, a healthy condition, or a combination of a second plurality of plant diseases; obtaining a second digital model for classifying an image into a class of a second set of classes corresponding to the second plurality of plant diseases; receiving a new image from a user device; applying the first digital model to a plurality of first regions within the new image to obtain a plurality of classifications; applying the second digital model to one or more second regions, each corresponding to a combination of multiple first regions of the plurality of first regions, to obtain one or more classifications, the multiple first regions being classified into the class corresponding to the combination of the second plurality of plant diseases; transmitting classification data related to the plurality of classifications into a class corresponding to one of the first plurality of plant diseases or the healthy condition and the one or more classifications to the user device.

Systems and methods for a machine learning system to learn a new skill without catastrophically forgetting an existing skill and to continually learn in a self-supervised manner during operation, without human intervention.

An example apparatus for mining multi-scale hard examples includes a convolutional neural network to receive a mini-batch of sample candidates and generate basic feature maps. The apparatus also includes a feature extractor and combiner to generate concatenated feature maps based on the basic feature maps and extract the concatenated feature maps for each of a plurality of received candidate boxes. The apparatus further includes a sample scorer and miner to score the candidate samples with multi-task loss scores and select candidate samples with multi-task loss scores exceeding a threshold score.

State of the art systems and methods attempting to generate synthetic biosignals such as PPG generate patient specific PPG signatures and do not correlate with pathophysiological changes. Embodiments herein provide a method and system for generating synthetic time domain signals to build a classifier. The synthetic signals are generated using statistical explosion. Initially, a parent dataset of actual sample data of class and non-class subjects is identified, and statistical features are extracted. Kernel density estimate (KDE) is used to vary the feature distribution and create multiple data template from a single parent signal. PPG signal is again reconstructed from the distribution pattern using non-parametric techniques. The generated synthetic data set is used to build the two stage cascaded classifier to classify CAD and Non CAD, wherein the classifier design enables reducing bias towards any class.

An Artificial Intelligence (AI) based automatic damage detection and estimation system receives images of a damaged object. The images are converted into monochrome versions if needed and analyzed by an ensemble machine learning (ML) cause prediction model that includes a plurality of sub-models that are each trained to identify a cause of damage to a corresponding portion for the damaged object from a plurality of causes. In addition, an explanation for the selection of the cause from the plurality of causes is also provided. The explanation includes image portions and pixels of images that enabled the cause prediction model to select the cause of damage. An ML parts identification model is also employed to identify and labels parts of the damaged object which are repairable and parts that are damaged and need replacement. The cost estimation for the repair and restoration of the damaged object can also be generated.

Provided are a neural network training method and apparatus for image retrieval, and an electronic device. A neural network includes: one feature extractor and a plurality of learners. The method includes: for each training image group, inputting three images of the training image group into the feature extractor, and determining features of the three images (501); for each image in each training image group, respectively multiplying the features of the image by a random weight corresponding to each learner, so as to obtain weighted features corresponding to each learner (502); for each image in each training image group, inputting the weighted features of the image corresponding to each learner into the corresponding learner, and determining a plurality of feature vectors of the image (503); and adjusting parameters of the neural network on the basis of the plurality of feature vectors of each image in a plurality of training image groups (504). The influence of information differences between training data on a network is weakened.