A regional precipitation nowcasting system based on cycle-generative adversarial network (GAN) extension includes an input unit configured to receive an input composite hybrid surface rainfall (HSR) image including precipitation information of a region of interest corresponding to a first time, a cycle-GAN configured to generate a resultant composite HSR image including precipitation information of the region of interest corresponding to a second time which comes later than the first time on the basis of the input composite HSR image using a first cycle-GAN and a second cycle-GAN which is complementary to the first cycle-GAN, and an output unit configured to output the resultant composite HSR image as a nowcasting image of the region of interest. The regional precipitation nowcasting system and method based on cycle-GAN extension can ensure robust temporal causality by applying pixel losses to a cycle-GAN.

Embodiments of the present disclosure relate to a method, an electronic device, and a computer program product for acquiring an image. The method includes distilling an original image set through a capsule neural network model to generate a distilled image set, wherein the distilled image set includes a plurality of distilled images. The method further includes acquiring a first feature of a first image through the capsule neural network model. The method further includes acquiring a plurality of distilling features of the plurality of distilled images respectively through the capsule neural network model. The method further includes determining a plurality of similarities between the first feature and the plurality of distilling features respectively. The method further includes acquiring at least one original image matching the first image based on the plurality of similarities.

In an approach to integrating real-world properties into machine learning training, a real-world image is received. The real-world image is compared to a simulated image, where the comparison is performed using a discriminator network of a generative adversarial network (GAN). A generator network of the GAN is trained with results of the comparison of the real-world image to the simulated image. Responsive to determining that the real-world image is not optimal, the real-world image is iteratively tuned, using the generator network of the GAN, until it is determined that the real-world image is optimal, where the real-world image is optimal if the real-world image meets a predetermined threshold for accuracy of one or more image parameters of the simulated image versus the real-world image. The discriminator network of the GAN is trained with the real-world image.

Techniques are provided for increasing the accuracy of automated classifications produced by a machine learning engine. Specifically, the classification produced by a machine learning engine for one photo-realistic image is adjusted based on the classifications produced by the machine learning engine for other photo-realistic images that correspond to the same portion of a 3D model that has been generated based on the photo-realistic images. Techniques are also provided for using the classifications of the photo-realistic images that were used to create a 3D model to automatically classify portions of the 3D model. The classifications assigned to the various portions of the 3D model in this manner may also be used as a factor for automatically segmenting the 3D model.

An information processing apparatus is provided and includes an obtaining unit obtains an estimation result of a score representing a likelihood for each of a first candidate and a second candidate for a label to be added as an annotation to data to be annotated. A control unit controls processing for displaying, depending on the score for each of the first candidate and the second candidate, first display information and second display information through an output unit, the first display information indicating a display position associated with the first candidate, the second display information indicating a display position associated with the second candidate.

Facilitating the description or configuration of a computer vision model by generating a data structure comprising a plurality of language entities defining a semantic mapping of visual parameters to a visual parameter space based on a sensitivity analysis of the computer vision model.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for privacy preserving training of a machine learning model.

The disclosure discloses a method and a system for generating image adversarial examples based on an acoustic wave. The method includes: acquiring an image containing a target object or a target scene; generating simulated image examples for the acquired image, wherein the simulated image examples have adversarial effects on a deep learning algorithm in a target machine vision system; optimizing the generated simulated image examples to obtain an optimal adversarial example and corresponding adversarial parameters; and injecting the adversarial parameters into an inertial sensor of the target machine vision system in a manner of an acoustic wave, such that the adversarial parameters are used as sensor readings that will cause an image stabilization module in the target machine vision system to operate to generate particular blurry patterns in a generated real-world image so as to generate an image adversarial example in a physical world.