Systems and methods are provided for identifying a product in an image and outputting stock keeping units of the product. The system comprises three main components: a database server, a data analytics system and a standard dashboard. The database server contains real-time inventory images as well as historical images of each product type. The data analytics system is executed by a computer processor configured to apply a multi-head self-supervised learning-based classifier to detect product information captured by the image. The data analytics system is also configured to determine hierarchical classification categories for the product. The standard dashboard is configured to output a report regarding the product information.

A task appropriateness determination apparatus includes a first learning unit causing artificial intelligence (AI) to learn image information of an index indicating a target object in a task appropriately completed state, an appropriate image provider providing an image possibly including the target object in the appropriately completed state and the index, a second learning unit causing the AI to detect an image where the index is present from the provided image after learning of the index, and learn image information of the target object in the image where the index is present, an image capturing unit capturing an image of a region including the target object and the index at least after the task, and a task appropriateness determiner determining that the task has been appropriately performed in response to detection by the AI of the target object roughly identical to the learned target object in the appropriately completed state.

An information processing device includes an acquisition unit that acquires learned models for executing object detection by methods different from each other and a plurality of pieces of unlabeled learning data as a plurality of images including an object, an object detection unit that performs the object detection on each of the plurality of pieces of unlabeled learning data by using the learned models, a calculation unit that calculates a plurality of information amount scores indicating values of the plurality of pieces of unlabeled learning data based on a plurality of object detection results, and a selection output unit that selects a predetermined number of pieces of unlabeled learning data from the plurality of pieces of unlabeled learning data based on the plurality of information amount scores and outputs the selected unlabeled learning data.

In some implementations a neural network is trained to perform a main task using a clustering constraint, for example, using both a main task training loss and a clustering training loss. Training inputs are inputted into a main task neural network to produce output labels predicting locations of the parts of the objects in the training inputs. Data from pooled layers of the main task neural network is inputted into a clustering neural network. The main task neural network and the clustering neural network are trained based on a main task loss from the main task neural network and a clustering loss from the clustering neural network. The main task loss is determined by comparing differences between the output labels and the training labels. The clustering loss encourages the clustering network to learn to label the parts of the objects individually, e.g., to learn groups corresponding to the object parts.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing images using an image processing neural network system. One of the systems includes a domain transformation neural network implemented by one or more computers, wherein the domain transformation neural network is configured to: receive an input image from a source domain; and process a network input comprising the input image from the source domain to generate a transformed image that is a transformation of the input image from the source domain to a target domain that is different from the source domain.

This disclosure relates to improved techniques for performing computer vision functions on medical images, including object segmentation functions for identifying medical objects in the medical images and grading functions for determining severity labels for medical conditions exhibited in the medical images. The techniques described herein utilize a neural network architecture to perform these and other functions. The neural network architecture can be trained, at least in part, using semi-supervised learning techniques that enable the neural network architecture to accurately perform the object segmentation and grading functions despite limited availability of pixel-level annotation information.

Aspects of the present disclosure provide systems, methods, and computer-readable storage media that support adaptive machine learning (ML) classification that mitigates effects of catastrophic forgetting while reducing overall resource requirements. To illustrate, a computing device may train first and second ML classifiers based on historical streamed data. The second ML classifier is trained to use continuous learning, and the first ML classifier is not. If data drift of a data stream is below a lower threshold, the data stream is provided as input to the first ML classifier to generate classification output (e.g., predictions). If the data drift is above the lower threshold, dynamic switching occurs and the data stream is provided as input to the second ML classifier instead of the first ML classifier to generate the classifier output. If the data drift is above an upper threshold, operations are performed to train new ML classifiers.

A method for operating a computing system on at least one processor includes performing search space reduction on input data using a first trained artificial intelligence model to generate relevant regions in the input data. The method also includes generating region proposals in the relevant regions using a second trained artificial intelligence model. The method further includes performing unsupervised anomaly classification on the region proposals using a third trained artificial intelligence model to classify each of the region proposals as normal or as an anomaly. The method further includes performing contextual filtering on the region proposals classified as anomalies to determine if any of the region proposals classified as anomalies are contextually normal using a fourth trained artificial intelligence model.

A training system includes: a model; and a training module configured to: construct a first pair of images of at least a first portion of a first human captured at different times; construct a second pair of images of at least a second portion of a second human captured at the same time from different points of view; input the first and second pairs of images to the model; the model configured to: generate first and second reconstructed images of the at least the first portion of the first human based on the first and second pairs, respectively, and the training module is configured to selectively adjust one or more parameters of the model based on: the first reconstructed image and the second reconstructed image.

An image processing system includes a memory to store a classifier and a set of labeled images for training the classifier, wherein each labeled image is labeled as either a positive image that includes an object of a specific type or a negative image that does not include the object of the specific type, wherein the set of labeled images has a first ratio of the positive images to the negative images. The system includes an input interface to receive a set of input images, a processor to determine a second ratio of the positive images, to classify the input images into positive and negative images to produce a set of classified images, and to select a subset of the classified images having the second ratio of the positive images to the negative images, and an output interface to render the subset of the input images for labeling.