Abstract: A method performed by one or more processors for determining an optimal hyperparameter value for a hyperparameter among hyperparameters of an artificial intelligence (AI) model, the method including: performing first pre-trainings of the AI model using a first low-resolution image set according to sets of values of hyperparameters, each set of values including values respectively corresponding to the hyperparameters, wherein the low-resolution image set includes first low-resolution images that are low-resolution relative to a resolution that the AI model is capable of performing inference on; performing first fine-tunings of the AI model using a second low-resolution image set, wherein the first fine-tunings are performed according to the respectively corresponding sets of values of the hyperparameters; and determining the optimal hyperparameter value based on metrics of performance of the AI model for which the first pre-trainings and the first fine-tunings have been performed.

Abstract: An electronic device including defect generating circuitry configured to generate a defect image based on defect information and a normal image, characteristic comparison circuitry configured to determine a validity of the defect image based on a difference value between outputs obtained by inputting the defect image into a first model trained to output a first value and a second model trained to output a second value, the second value being a same value as the first value responsive to the defect image being analyzed as the normal image, and defect detecting circuitry configured to train a defect detection model to detect defects in a subject image based on the defect image.

Abstract: A processor-implemented method includes removing one or more patch images from an original image comprising a plurality of patch images to generate a transformed image, and training a transformer model to minimize a value of a loss function for a difference between a result of inputting the original image and a result of inputting the transformed image.

Abstract: A method and device with model selection are provided. The method includes identifying one or more models trained using a first data set, the first data set associated with a source domain and a second data set associated with a target domain, for each of the one or more models, acquiring at least one first feature corresponding to at least one piece of first data from the first data set and acquiring at least one second feature corresponding to at least one piece of second data from the second data set, acquiring, based on the at least one first feature, at least one third feature with a set dimension corresponding to the at least one second feature, and selecting a target model from the one or more models based on a first score calculated using the at least one first feature and the at least one third feature.

Abstract: Disclosed is a method that includes generating a prediction consistency value that indicates a consistency of prediction of an object in an input image with respect to class prediction values for the object in an input image from classification models to which the input image is input, and identifying a class of the object. Identifying the class of the object includes, in response to a class type being determined, based on the prediction consistency value, of the object being determined to correspond to a majority class, identifying a class of the object based on a corresponding class prediction value output for the object from a majority class prediction model, and in response to the class type of the object being determined to correspond to a minority class, identifying the class of the object based on another corresponding class prediction value output for the object from a minority class prediction model.

Abstract: A meta continual-learning and inferring method uses an implementation of Bayes' theorem, and includes: calculating a likelihood of learning data for a given latent variable by a data distribution learner; performing a sequential Bayesian update and calculating a final posterior distribution of the latent variable by using prior distribution of the latent variable and the calculated likelihood by a Bayes' calculator; sampling the latent variable from the final posterior distribution; and inferring test output data based on the sampled latent variable and test input data by an inference engine, wherein respective meta parameters of a neural network of the data distribution learner, the prior distribution of the latent variable of the Bayes' calculator, and a neural network of the inference engine are trained by a meta learning.

Abstract: A method of updating a sequence model for meta-continual learning, the method including generating an episode comprising a training dataset and a test dataset, updating an internal state of the sequence model by performing a forward pass of the training dataset on the sequence model, wherein the internal state is updated based on a parameter of the sequence model, generating an output corresponding to a test input included in the test dataset by performing a forward pass of the test input on the sequence model based on the updated internal state and the parameter, determining a difference between the output corresponding to the test input and a target test result corresponding to the test input as a meta-loss, and updating the parameter of the sequence model based on the meta-loss.

Abstract: An electronic device includes one or more processors configured to select a classification model for classifying an image from among classification models based on additional information of the image by using an artificial intelligence (AI) model, and classify the image by using the selected classification model.

Abstract: A device for measuring performance of an artificial intelligence (AI) model includes: one or more processors and a memory; and the memory storing instructions configured to cause the one or more processors to perform a process including: determining perturbations for respective classes based on respective class importances and adding noises determined based on the respective perturbations to respective representative vectors of the respective classes; and generating an inference uncertainty of the AI model from inference results outputted by the AI model using a weight matrix including the noise-added representative vectors.

Abstract: Disclosed is a method that includes generating a prediction consistency value that indicates a consistency of prediction of an object in an input image with respect to class prediction values for the object in an input image from classification models to which the input image is input, and identifying a class of the object. Identifying the class of the object includes, in response to a class type being determined, based on the prediction consistency value, of the object being determined to correspond to a majority class, identifying a class of the object based on a corresponding class prediction value output for the object from a majority class prediction model, and in response to the class type of the object being determined to correspond to a minority class, identifying the class of the object based on another corresponding class prediction value output for the object from a minority class prediction model.

Abstract: A processor-implemented method includes: determining a prediction loss based on class prediction data obtained by applying a first machine learning model to a training input and a class label with which the training input is labeled; determining a confidence of the class label based on the determined prediction loss; and training a second machine learning model using the training input based on the determined confidence.

Abstract: A double-sided keypad (42) comprises a first portion (52), a second portion (54), and a plurality of spacers (40). The first portion (52) comprises a first substrate (34), a first conductive plate (36) coupled on one side to the first substrate (34), and a first input/output line (37) and a second input/output line (38) that are coupled to the first conductive plate (36). The second portion (54) is symmetrically inverted from the first portion (52). The second portion (54) comprises a second substrate (48), a second conductive plate (44) coupled on one side to the second substrate (48), and a third input/output line (39) and a fourth input/output line (41) that are coupled to the second conductive plate (44). The plurality of spacers (40) are coupled between the first conductive plate (36) of the first portion (52) and the second conductive plate (44) of the second portion.