Techniques are disclosed for dynamic time-based custom model generation as part of infrastructure-as-a-service (IaaS) environment. A custom model generation service may receive a set of training data and a time-based constraints for training a machine learning model. The custom model generation service may subsample the training data and generate a set of optimized tuned hyperparameters for a machine learning model to be trained using the subsampled training data. An experimental interval time of training is determined and the machine learning model is trained on the subsampled training data according to the optimized tuned hyperparameters over a set of training intervals similar to the experimental time interval. A customized machine learning model trained in the time-based constraint is output. The hyperparameter tuning may be performed using a modified mutating genetic algorithm for a set of hyperparameters to determine the optimized tuned hyperparameters prior to the training.

A method may include receiving an event message from a home security edge device, including image data. The method may include determining whether the image data represents a false trigger event based on inputting the image data into an artificial intelligence model and/or receiving a user input. The user input may be responsive to a presentation of the image data. If the image data represents the false trigger event, the method may include generating training data for retraining the artificial model. The training data may include a portion of the image data. The method may include updating a local dataset to include the training data and training the artificial intelligence model. The method may include transmitting the training data to a central database. If the image data does not represent a false trigger event, the method may include providing a security alert for display on one or more user devices.

Provided are an information processing apparatus, an information processing method, and a program capable of accumulating appropriate relearning data. An information processing apparatus includes an input unit that inputs input data to a learned model acquired in advance through machine learning using learning data, an acquisition unit that acquires output data output from the learned model through the input using the input unit, a reception unit that receives correction performed by a user for the output data acquired by the acquisition unit, and a storage controller that performs control for storing, as relearning data of the learned model, the input data and the output data that reflects the correction received by the reception unit in a storage unit in a case where a value indicating a correction amount acquired by performing the correction for the output data is equal to or greater than a threshold value.

Systems and techniques are disclosed for improvement of machine learning systems based on enhanced training data. An example method includes providing a visual concurrent display of a set of images of features, the features requiring classification by a reviewing user. The user interface is provided to enable the reviewing user to assign classifications to the images, the user interface being configured to create, read, update, and/or delete classifications. The user interface is responsive to the user, with the user response indicating at least two images with a single classification. The user interface is updated to represent the single classification.

A method for checking samples for defects is provided, in which image data of the samples are recorded and classified into predeterminable defect categories by a defect detection algorithm, and the samples classified into a defect category are represented in a multi-dimensional confusion matrix as a classification result of the defect detection algorithm, characterized in that—miniature images which reproduce the image data are assigned according to the classified defect categories of the image data to segments of the confusion matrix which represent the defect categories, and these miniature images are displayed visually, —the miniature image is assigned by an interaction with a user or a software robot to a different segment from the assigned segment of the confusion matrix, and is either provided as training image data for the defect detection algorithm or is output as training image data for the defect detection algorithm.

A method of reducing false positives and identifying relevant true alerts in a video management system includes analyzing images to look for patterns indicating changes between subsequent images. When a pattern indicating changes between subsequent images is found, the video management system solicits from a user an indication of whether the pattern belongs to one of two or more predefined categories. The patterns indicating changes between subsequent images are saved for subsequent use. Subsequent images received from the video camera are analyzed to look for patterns indicating changes between subsequent images. When a pattern indicating changes between subsequent images is detected by the video management system, the video management system compares the pattern indicating changes between subsequent images to those previously categorized into one of the two or more predefined categories. Based on the comparison, the video management system may provide an alert to the user.

A method for the computer-aided training of an artificial neural network (ANN) for recognizing structural features on objects, by means of which method identified structural features on objects are recognizable rapidly and reliable. That is achieved by virtue of the fact that a convolutional neural network (CNN) having a multiplicity of neurons is used for the training of an ANN for feature recognition on objects. Said network comprises a multiplicity of convolutional and/or pooling layers for the extraction of information from images of individual objects. In this case, the images of the objects are respectively scaled or scaled up and/or down from layer to layer. During the scaling of the images information about the structural features of the objects is maintained, specifically independently of the scaling of the images.

In a learning phase, a processor of a sample observation device: stores design data on a sample in a storage resource; creates a first learning image as a plurality of input images; creates a second learning image as a target image; and learns a model related to image quality conversion with the first and second learning images. In a sample observation phase, the processor obtains, as an observation image, a second captured image output by inputting a first captured image obtained by imaging the sample with an imaging device to the model. The processor creates at least one of the first and second learning images based on the design data.

The technology disclosed implements Human-in-the-loop (HITL) active learning with a feedback look via a user interface that is expressly designed for the suggested images to admit multiple fast feedbacks, including selection, dismissal, and annotation. Then, the downstream selection policy for subsequent sampling iterations is based on the available data interpreted in the context of the previous selections, dismissals, and annotations.

A method is provided for identifying consumer packaged goods (CPGs). The method includes providing to a machine learning classifier a set of images containing at least one CPG; receiving from the machine learning classifier an indication that the machine learning classifier cannot reliably identify a designated CPG in the set of images; determining whether the designated CPG is a product in a product catalog; if the designated CPG is in the product catalog, then associating the designated CPG with a Global Trade Item Number (GTIN); and if the designated product is not in the product catalog, then designating the CPG as a potentially new product. Notably, this approach allows partially identified products to be treated as full-fledged members of the product catalog, thus allowing data to be collected on these products even before they have been fully identified and their GTINs have been resolved.