Determining confident data samples for machine learning models on unseen data

A system, comprising:

• a memory that stores computer executable components;
• and
• a processor that executes the computer executable components stored in the memory, wherein the computer executable components comprise:
• a feature extraction component that extracts a feature vector for a data sample based on projection of the data sample onto a standard feature space;
• and
• an outlier detection component that processes the feature vector using an outlier detection model to determine whether the data sample is within a scope of a training dataset used to train a machine learning model, wherein the outlier detection model was trained using features extracted from the training dataset based on projection of data samples included in the training dataset onto the standard feature space.

A machine-readable storage medium, comprising executable instructions that, when executed by a processor, facilitate performance of operations, comprising:

• projecting a data sample onto a standard feature space;
• extracting a feature vector for the data sample based on the projecting using a feature extraction network trained on the standard feature space;
• and
• classifying the data sample as an outlier data sample or an inlier data sample relative to a training dataset used to train a machine learning model, wherein the classifying comprises processing the feature vector using an outlier detection model that was trained using features extracted from the training dataset based on projection of the training dataset onto the standard feature space.