Abstract: A method for providing a combined training data set for a machine learning model includes (i) providing image data, wherein the image data comprises a non-labelled portion and a labelled portion, (ii) training a base machine learning model based on the non-labelled portion of the image data to provide a generalized model, (iii) training the generalized model based on the labelled portion of the image data to provide a semantic segmentation model, (iv) analyzing a training data set with the semantic segmentation model to provide a semantics for the training data set, (v) analyzing the training data set with a zero-shot segmentation model to provide segmentation for the training data set, (vi) providing the combined training data set based on a combination of the provided semantics and the provided segmentation of the training data set. A computer program, a device, and a storage medium for this purpose are also disclosed.

Abstract: Methods and systems are disclosed for generating training data for a machine learning model for better performance of the model. A source image is selected from an image database, along with a target image. An image segmenter is utilized with the source image to generate a source image segmentation mask having a foreground region and a background region. The same is performed with the target image to generate a target image segmentation mask having a foreground region and a background region. Foregrounds and backgrounds of the source image and target image are determined based on the masks. The target image foreground is removed from the target image, and the source image foreground is inserted into the target image to create an augmented image having the source image foreground and the target image background. The training data for the machine learning model is updated to include this augmented image.

Abstract: A method for evaluating a training data set for a machine learning model includes (i) providing sensor data, wherein a portion of the sensor data comprises a detection feature, (ii) generating synthetic data by another machine learning model based on the portion of the sensor data having the detection feature, (iii) determining a ratio between a fraction of synthetic data and a fraction of sensor data having the detection feature for the training data set, and (iv) evaluating the training data set by way of the determined ratio based on at least one metric. Also disclosed is a computer program, device, and a storage medium for this purpose.

Abstract: A method for verifying a machine learning algorithm. The method includes: providing test data sets for testing a machine learning algorithm trained based on a training data set, wherein none of the test data sets has a common element with the training data set; respectively ascertaining, for each of the plurality of test data sets, a value of the similarity between the corresponding test data set and the training data set; respectively generating, for each of the plurality of test data sets, a test result by testing the machine learning algorithm based on the elements of the corresponding test data set; verifying the machine learning algorithm based on the values of the similarity of all of the plurality of test data sets and the test results of all of the plurality of test data sets in order to generate verification results; and providing the verification results.

Abstract: A method for prioritizing training examples in a training data set for a classifier designed to map measurement data to classification scores with respect to classes of a predetermined classification. In the method includes: the classifier is trained with the training examples from the training data set; modifications are generated for at least one training example; classification scores are respectively determined from the modifications by means of the classifier; a priority of the training example to which the modifications belong is determined from the distribution of these classification scores.

Abstract: Methods and systems are disclosed for generating training data for a machine learning model for better performance of the model. A source image is selected from an image database, along with a target image. An image segmenter is utilized with the source image to generate a source image segmentation mask having a foreground region and a background region. The same is performed with the target image to generate a target image segmentation mask having a foreground region and a background region. Foregrounds and backgrounds of the source image and target image are determined based on the masks. The target image foreground is removed from the target image, and the source image foreground is inserted into the target image to create an augmented image having the source image foreground and the target image background. The training data for the machine learning model is updated to include this augmented image.

Abstract: A method for detecting an anomalous image among a dataset of images using an Adversarial Autoencoder includes training an Adversarial Autoencoder in a first training with a training dataset of images, with the Adversarial Autoencoder being optimized such that a distribution of latent representations of images of the training dataset of images approaches a predetermined prior distribution and that a reconstruction error of reconstructed images of the training dataset of images is minimized. Subsequently, anomalies are detected in the latent representation and the Adversarial Autoencoder is trained in a second training with the training dataset of images, but taking into account the detected anomalies. The anomalous image among the first dataset of images is detected by the trained Adversarial Autoencoder dependent on at least one of the reconstruction error of the image and a probability density under the predetermined prior distribution.

Abstract: A method for ascertaining whether a series of sensor values contains an anomaly, including the following steps: providing a shapelet and at least one training data series; measuring in each case a distance between the shapelet and the training data series at a plurality of different predefinable positions of the training data series; ascertaining at least one minimal distance from the measured distances and ascertaining at least one change variable for at least one predefinable data point of the shapelet the change variable being ascertained as a function of at least one of the measured distances. A computer program, a device for carrying out the method, and a machine-readable memory element, on which the computer program is stored are also provided.

Abstract: A method for ascertaining whether a series of sensor values contains an anomaly, including the following steps: providing a shapelet and at least one training data series; measuring in each case a distance between the shapelet and the training data series at a plurality of different predefinable positions of the training data series; ascertaining at least one minimal distance from the measured distances and ascertaining at least one change variable for at least one predefinable data point of the shapelet the change variable being ascertained as a function of at least one of the measured distances. A computer program, a device for carrying out the method, and a machine-readable memory element, on which the computer program is stored are also provided.

Abstract: A method for detecting an anomalous image among a dataset of images using an Adversarial Autoencoder includes training an Adversarial Autoencoder in a first training with a training dataset of images, with the Adversarial Autoencoder being optimized such that a distribution of latent representations of images of the training dataset of images approaches a predetermined prior distribution and that a reconstruction error of reconstructed images of the training dataset of images is minimized. Subsequently, anomalies are detected in the latent representation and the Adversarial Autoencoder is trained in a second training with the training dataset of images, but taking into account the detected anomalies. The anomalous image among the first dataset of images is detected by the trained Adversarial Autoencoder dependent on at least one of the reconstruction error of the image and a probability density under the predetermined prior distribution.