A system and a method for improved generation of 3D avatars for virtual try-on of garments is provided. Inputs from a first user type are received, via a first input unit, for generating one or more garment types in a graphical format. Further, a 3D avatar of a second user type is generated in a semi-automatic manner or an automatic manner based on capturing a first input type or a second input type respectively received via a second input unit. The first input type comprises measurements of body specifications of the second user type and the second input type comprises body images of the second user type. Further, the generated garments are rendered on the generated 3D avatar of the second user type for carrying out a virtual try-on operation.

A recording medium stores a program for causing a computer to execute processing including: estimating a low-dimensional feature quantity with a lower dimensionality than input data obtained by encoding the input data as a conditional probability distribution using a condition based on data in a peripheral area of data of interest in the input data; and adjusting parameters of each of the encoding and the estimating and decoding of a feature quantity obtained by adding a noise to the low-dimensional feature quantity, based on a cost that includes output data obtained by the decoding, an error between the output data and the input data, and entropy of the conditional probability distribution. In determining whether input data to be determined is normal using the adjusted parameters, the determination is performed based on the conditional probability distribution based on data of a peripheral area of the input data to be determined.

A recording medium stores a program for causing a computer to execute processing including: estimating a low-dimensional feature quantity with a lower dimensionality than input data obtained by encoding the input data as a conditional probability distribution using a condition based on data in a peripheral area of data of interest in the input data; and adjusting parameters of each of the encoding and the estimating and decoding of a feature quantity obtained by adding a noise to the low-dimensional feature quantity, based on a cost that includes output data obtained by the decoding, an error between the output data and the input data, and entropy of the conditional probability distribution. In determining whether input data to be determined is normal using the adjusted parameters, the determination is performed based on the conditional probability distribution based on data of a peripheral area of the input data to be determined.

According to the present invention, a method for continuous learning of object anomaly detection and state classification model includes acquiring, by a detection and classification apparatus, information about a medium of anomaly detection from an inspection target; generating, by the detection and classification apparatus, an input value, which is a feature vector matrix including a plurality of feature vectors, from the medium information; deriving, by the detection and classification apparatus, a restored value imitating the input value through a detection network learned to generates the restored value for the input value; determining, by the detection and classification apparatus, whether a restoration error indicating a difference between the input value and the restored value is greater than or equal to a previously calculated reference value; and storing, by the detection and classification apparatus, the input value as normal data upon determining that the restoration error is less than the reference value.

According to the present invention, a method for continuous learning of object anomaly detection and state classification model includes acquiring, by a detection and classification apparatus, information about a medium of anomaly detection from an inspection target; generating, by the detection and classification apparatus, an input value, which is a feature vector matrix including a plurality of feature vectors, from the medium information; deriving, by the detection and classification apparatus, a restored value imitating the input value through a detection network learned to generates the restored value for the input value; determining, by the detection and classification apparatus, whether a restoration error indicating a difference between the input value and the restored value is greater than or equal to a previously calculated reference value; and storing, by the detection and classification apparatus, the input value as normal data upon determining that the restoration error is less than the reference value.

A computer-implemented method comprising: accessing a machine learning, ML, model that is operable to sample a causal graph from a graph distribution describing different possible graphs, wherein nodes represent the different variables of said set and edges represent causation, and the graph distribution comprises a matrix of probabilities of existence and causal direction of potential edges between pairs of nodes, and wherein the ML model is trained to be able to generate a respective simulated value of a selected variable from among said set based on the sampled causal graph. The method further comprises using the ML model to estimate a treatment effect from one or more intervened-on variables on another, target variable from among the variables of said set.

Herein are machine learning techniques that adjust reconstruction loss of a reconstructive model such as an autoencoder based on importances of values of features. In an embodiment and before, during, or after training, the reconstructive model that more or less accurately reconstructs its input, a computer measures, for each distinct value of each feature, a respective importance that is not based on the reconstructive model. For example, importance may be based solely on a training corpus. For each feature during or after training, a respective original loss from the reconstructive model measures a difference between a value of the feature in an input and a reconstructed value of the feature generated by the reconstructive model. For each feature, the respective importance of the input value of the feature is applied to the respective original loss to generate a respective weighted loss. The weighted losses of the features of the input are collectively detected as anomalous or non-anomalous.

Herein are feature extraction mechanisms that receive parsed log messages as inputs and transform them into numerical feature vectors for machine learning models (MLMs). In an embodiment, a computer extracts fields from a log message. Each field specifies a name, a text value, and a type. For each field, a field transformer for the field is dynamically selected based the field's name and/or the field's type. The field transformer converts the field's text value into a value of the field's type. A feature encoder for the value of the field's type is dynamically selected based on the field's type and/or a range of the field's values that occur in a training corpus of an MLM. From the feature encoder, an encoding of the value of the field's typed is stored into a feature vector. Based on the MLM and the feature vector, the log message is detected as anomalous.

Accuracy of a model extracting a graph structure as an intermediate representation from input data is improved. An encoding unit (100) extracts a feature amount of each of a plurality of vertices included in a graph structure (Tr) from input data (10), and calculates a likelihood that an edge is connected to the vertex. A sampling unit (130) determines the graph structure (Tr) based on a conversion result of a Gumbel-Softmax function for the likelihood. A learning unit (150) optimizes a decoding unit (140) and the encoding unit (100) by back propagation using a loss function including an error (L.sub.P) between output data (20) generated from the graph structure (Tr) and correct data.

This application discloses a photographing method and apparatus, to overcome blurring that occurs during photographing. When a zoom ratio is greater than a first zoom ratio threshold, a long-focus camera is started to capture an image. A zoom ratio of the long-focus camera is greater than or equal to the first zoom ratio threshold. Because a high zoom ratio causes large shake, a rotational blur occurs in the image. According to the photographing method disclosed in this application, a first neural network model for rotational image deblurring is used to implement rotational image deblurring processing. In this way, high imaging quality of an image, a video, or a preview image is presented to a user to some extent, and the imaging effect may not be inferior to the effect of photographing with a tripod.