A computing platform may be configured to (i) train an initial model object for a data science model using a machine learning process, (ii) determine that the initial model object exhibits a threshold level of bias, and (iii) thereafter produce an updated version of the initial model object having mitigated bias by (a) identifying a subset of the initial model object's set of input variables that are to be replaced by transformations, (b) producing a post-processed model object by replacing each respective input variable in the identified subset with a respective transformation of the respective input variable that has one or more unknown parameters, (c) producing a parameterized family of the post-processed model object, and (d) selecting, from the parameterized family of the post-processed model object, one given version of the post-processed model object to use as the updated version of the initial model object for the data science model.

A calculation unit calculates a first error for a boundary region of an image represented by image data, calculates a second error for a non-boundary region different from the boundary region, and calculates an error between label data and an estimation result based on the first error and the second error. And an influence of the first error on the calculation by the calculation unit is controlled to be smaller than an influence of the second error on the calculation by the calculation unit.

A calculation unit calculates a first error for a boundary region of an image represented by image data, calculates a second error for a non-boundary region different from the boundary region, and calculates an error between label data and an estimation result based on the first error and the second error. And an influence of the first error on the calculation by the calculation unit is controlled to be smaller than an influence of the second error on the calculation by the calculation unit.

A machine-learning model that is using production data and is operating in a production environment within a data-sensitive realm is analyzed, where this model was trained using a training dataset. An accuracy of the model is identified as falling below an accuracy threshold when providing one or more predictions of a subset of the production data. At least one characteristic of the production data that is used to predict the subset of the production data is determined to be underrepresented in the training dataset. The one or more predictions and the at least one characteristic are provided to a location outside of the production environment.

Provided are a processing method and system for a convolutional neural network, and a computer-readable medium, the processing method includes training a generator and training a discriminator, wherein training a generator includes: extracting a low-resolution color image from a high-resolution color image; training parameters of a generator network, by using the low-resolution color image and a noise image as an input image, based on parameters of a discriminator network, and reducing a generator cost function; training a discriminator includes: inputting an output image of the trained generator network and the high-resolution color image to the discriminator network, respectively; training parameters of the discriminator network by reducing a discriminator cost function (S204) the generator cost function and the discriminator cost function represent a degree in which the output image of the generator network corresponds to the high-resolution color image.

The present teaching relates to method, system, medium, and implementation of in-situ perception in an autonomous driving vehicle. A plurality of types of sensor data are acquired continuously via a plurality of types of sensors deployed on the vehicle, where the plurality of types of sensor data provide information about surrounding of the vehicle. One or more items surrounding the vehicle are tracked, based on some models, from a first of the plurality of types of sensor data from a first type of the plurality of types of sensors. A second of the plurality of types of sensor data are obtained from a second type of the plurality of sensors and are used to generate validation base data. Some of the one or more items are labeled, automatically, via validation base data to generate labeled at least some item, which is to be used to generate model updated information for updating the at least one model.

This disclosure relates to a picture generation method and device, a storage medium, and an electronic device. The method includes: obtaining a source portrait picture displaying a target object; cropping the source portrait picture to obtain a face region picture corresponding to a face of the target object excluding a hair portion; inputting the face region picture to a picture generation model to obtain an output result of the picture generation model, the picture generation model being obtained after machine learning training through an adversarial neural network model by using a plurality of sample pictures; and generating a target portrait picture by using the output result of the picture generation model, the target portrait picture displaying a target hairstyle matching the face of the target object. This disclosure resolves the technical problem that pictures generated in related art cannot achieve an effect expected by a user and other technical problems.

A method is for providing an aggregate algorithm for processing medical data. In an embodiment, a multitude of local algorithms are trained by machine learning. The training of each respective local algorithm is performed on a respective local system using respective local training data. A respective algorithm dataset concerning the respective local algorithm is transferred to an aggregating system that generates the aggregate algorithm based on the algorithm datasets.

A method and a system are described for context based clustering of one or more objects. The method comprises receiving, by the object clustering system, receiving, by an object clustering system, an object clustering request for one or more objects associated with a plurality of contextual parameters, where the plurality of contextual parameters comprises one or more physical attributes and one or more non-physical attributes. It further includes tagging the one or more non-physical attributes respectively to the one or more physical attributes. It further includes identifying a common context from the one or more physical attributes associated with the one or more objects based on the tagging. It further includes mapping the one or more physical attributes to the one or more objects based on the common context. It then includes clustering the one or more objects based on the mapping.

An information display device includes: a display section that displays a view of a situation ahead of a vehicle and guidance information; a learning section that classifies and learns targets appearing on a travel route and visible through the display section as: a first target necessary to be visible, a second target necessary to be visible under the setting condition, or a third target not necessary to be visible, and a display control section configured to: prohibit display of the guidance information in a prohibited region with the first target, permit display of the guidance information in a permitted region with the third target, and either prohibit display of the guidance information in a conditional region with the second target and the setting condition is met, or permit display of the guidance information in the conditional region with the second target and the setting condition is not met.