A deep learning-based method for calculating an overhang of a battery includes the following steps: obtaining a training sample image set; training a neural network according to the training sample image set to obtain a segmentation network model; detecting an object detection image of the battery to be detected according to the segmentation network model to obtain a corresponding first binarized image; obtaining top coordinates of each of a positive electrode and a negative electrode of the battery to be detected according to the first binarized image; and calculating the overhang of the battery to be detected according to the top coordinates.

Methods and systems for performing diagnostic functions for a deep learning model are provided. One system includes one or more components executed by one or more computer subsystems. The one or more components include a deep learning model configured for determining information from an image generated for a specimen by an imaging tool. The one or more components also include a diagnostic component configured for determining one or more causal portions of the image that resulted in the information being determined and for performing one or more functions based on the determined one or more causal portions of the image.

In various examples, a sequential deep neural network (DNN) may be trained using ground truth data generated by correlating (e.g., by cross-sensor fusion) sensor data with image data representative of a sequences of images. In deployment, the sequential DNN may leverage the sensor correlation to compute various predictions using image data alone. The predictions may include velocities, in world space, of objects in fields of view of an ego-vehicle, current and future locations of the objects in image space, and/or a time-to-collision (TTC) between the objects and the ego-vehicle. These predictions may be used as part of a perception system for understanding and reacting to a current physical environment of the ego-vehicle.

Embodiments described herein provide a system for facilitating efficient dataset management. During operation, the system obtains a first dataset comprising a plurality of elements. The system then determines a set of categories for a respective element of the plurality of elements by applying a plurality of AI models to the first dataset. A respective category can correspond to an AI model. Subsequently, the system selects a set of sample elements associated with a respective category of a respective AI model and determines a second dataset based on the selected sample elements.

A method for simulating a rendering of a makeup product on a body area including the steps of: acquiring an image of the body area without makeup of a subject, determining first color parameters of the pixels of the image corresponding to the body area without makeup, identifying the pixels of the body area without makeup exhibiting highest brightness or red component value, and determining second color parameters of the pixels of the image corresponding to the body area, wherein the second color parameters render a making up of the body area by the makeup product.

The present disclosure generally relates to optimizing device interrupt coalescing based upon host device behavior. The data storage device maintains three functional states: a training state, a holding state, and a retraining state. The data storage device switches between states based upon host device behavior as well as the behavior of the data storage device. Once the data storage device finds the optimum conditions for coalescing, the data storage device will periodically test the conditions to adapt to changing host device behavior as well as data storage device behavior. In so doing, the data storage device can dynamically adjust interrupt coalescing to ensure optimum operation of the storage device.

Methods, apparatus, systems, computing devices, computing entities, and/or the like for using machine-learning concepts (e.g., machine learning models) to determine predicted taxonomy-based classification scores for claims and dynamically update data fields based on the same.

A training method for an image denoising model that can include collecting multiple sample image groups through a shooting device, each sample image group including multiple frames of sample images with a same photographic sensitivity and sample images in different sample image groups having different photographic sensitivities. The method can further include acquiring a photographic sensitivity of each sample image group, determining a noise characterization image corresponding to each sample image group based on the photographic sensitivity, determining a training input image group and a target image associated with each sample image group, each training input image group including all or part of sample images in a corresponding sample image group and a corresponding noise characterization image, constructing multiple training pairs each including a training input image group and a target image, and training the image denoising model based on the multiple training pairs until the image denoising model converges.

Disclosed are embodiments of a installed software program that receive a model from a product management system. The model is trained to select one of a plurality of predefined states based on operational parameter values of the installation of the software program. Each of the plurality of predefined states define configuration values of the installation of the software program. The defined configuration values indicate, in some embodiments, updates to operational parameter values of the installation of the software program.

One or more computing devices, systems, and/or methods for hyper parameter optimization for machine learning ensemble generation are provided. For example, one or more base models are trained using diverse sets of hyper parameters, wherein different sets of hyper parameters (e.g., hyper parameters with different values) are used to train different base models. A matrix, populated with predictions from the set of base models, is generated. A machine learning ensemble is generated by processing the matrix utilizing a meta learner.