{'fraction_non_alphanumeric': 0.01406799531066823, 'fraction_numerical': 0.0005861664712778429, 'mean_word_length': 5.642023346303502, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Self-directed training of person reidentification with synthetic data

This paper examines the utility of a self-directed feedback training method for machine learning models trained on synthetic data. This method aims to improve the speed of data generation and training by generating small batches of training data and observing the classification performance for each class. The classification accuracy is then used to adjust subsequent training classes and data generation limiting the total generation and training time while achieving optimal performance. Synthetic generation of images provides a viable approach to training machine learning models when real data is sparse. Synthetic data removes the intensive and error-prone manual process of human data labeling through automatic tagging. This is particularly valuable for re-identification tasks where unique objects need to be identified from multiple cameras with different orientations, lighting, or focal characteristics. We construct an artificial re-identification scene using 3D modeling software and generate images with a number of human avatar objects taken from different orientations, backgrounds, and lighting conditions. Automatic tagging and bounding generates re-identification metadata allowing unique avatars to be recognized by a metric learning neural network. As the network improves, the classes with lowest performance prompt the generator to supply additional images to improve the classifier accuracy. This allows the rendering engine to focus on the dominant error cases. This process will be compared against the rendering/training time and accuracy of the same system trained without self-directed feedback training.