An information processing apparatus comprises a first generation unit configured to generate a synthesized image in which a second image is synthesized in a closed region in a first image, and a second generation unit configured to generate learning data, the learning data including a label and the synthesized image, the label indicating an object region including a region corresponding to the closed region in the synthesized image.

An information processing apparatus comprises a first generation unit configured to generate a synthesized image in which a second image is synthesized in a closed region in a first image, and a second generation unit configured to generate learning data, the learning data including a label and the synthesized image, the label indicating an object region including a region corresponding to the closed region in the synthesized image.

Embodiments of the present disclosure relate to a method, an electronic device, and a computer program product for data augmentation. The method includes: generating a group of candidate images based on a target image by using a thermodynamic genetic algorithm (TDGA) model, the TDGA model being configured to apply one or more operations of a set of predetermined image processing operations during each evolution process; and determining multiple augmented images from the group of candidate images based on free energy of the group of candidate images, the multiple augmented images being determined as belonging to the same classification with the target image. In this way, data augmentation can be efficiently implemented by a thermodynamic genetic algorithm.

Disclosed are a method for accelerating supervised training of a spiking neural network. The method includes measuring first and second membrane potentials for each time step during a training process, extracting distribution data of the first and second membrane potentials based on the first and second membrane potentials for the each time step, calculating a threshold value to be used in a subsequent training process based on the distribution data of the first and second membrane potentials, classifying images having no training contribution based on the threshold value calculated in a previous training process, and terminating the training at the time step based on determining that the image does not have the training contribution when a difference between the first and second membrane potentials in the time step is greater than the threshold value.

Disclosed are a method for accelerating supervised training of a spiking neural network. The method includes measuring first and second membrane potentials for each time step during a training process, extracting distribution data of the first and second membrane potentials based on the first and second membrane potentials for the each time step, calculating a threshold value to be used in a subsequent training process based on the distribution data of the first and second membrane potentials, classifying images having no training contribution based on the threshold value calculated in a previous training process, and terminating the training at the time step based on determining that the image does not have the training contribution when a difference between the first and second membrane potentials in the time step is greater than the threshold value.

Disclosed are a method for accelerating early determination training. The method for accelerating early determination training includes a timestep splitting operation of splitting a timestep, a membrane potential measuring operation of measuring first and second membrane potentials for each splitted timestep during a current training process, a threshold value calculation operation of calculating a threshold value to be used in a subsequent training process based on the first and second membrane potentials, and when a difference between the first and second membrane potentials in the splitted timestep is greater than the threshold value, an early training termination operation of determining that the image does not have the training contribution and terminating training at the splitted timestep.

Disclosed are a method for accelerating early determination training. The method for accelerating early determination training includes a timestep splitting operation of splitting a timestep, a membrane potential measuring operation of measuring first and second membrane potentials for each splitted timestep during a current training process, a threshold value calculation operation of calculating a threshold value to be used in a subsequent training process based on the first and second membrane potentials, and when a difference between the first and second membrane potentials in the splitted timestep is greater than the threshold value, an early training termination operation of determining that the image does not have the training contribution and terminating training at the splitted timestep.

A method and apparatus for recognizing a three-dimensional (3D) object based on deep learning are provided. An object recognition apparatus constructs a data set including a virtual image and a real image, in which the data set includes labeled data corresponding to the virtual image and the real image, and unlabeled data corresponding to the virtual image and the real image. The object recognition apparatus inputs the data set to a recognition model for pre-trained object recognition based on self-supervised learning to perform the object recognition and acquire object information according to the object recognition.

A method and apparatus for recognizing a three-dimensional (3D) object based on deep learning are provided. An object recognition apparatus constructs a data set including a virtual image and a real image, in which the data set includes labeled data corresponding to the virtual image and the real image, and unlabeled data corresponding to the virtual image and the real image. The object recognition apparatus inputs the data set to a recognition model for pre-trained object recognition based on self-supervised learning to perform the object recognition and acquire object information according to the object recognition.

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.