{'fraction_non_alphanumeric': 0.009313154831199068, 'fraction_numerical': 0.005820721769499418, 'mean_word_length': 5.231884057971015, '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}}
A strategy for training 3D object recognition models with limited training data using transfer learning

A typical issue with development of 3D image recognition models and systems is that 3D images often come with large volumes of data and require expensive computational cost for image reconstruction and model training. Transfer learning is a machine learning technique where a model trained on one task is reused on another related task. In this research, we propose a Transfer Learning method that allows training a 3D object recognition model with very limited training data, so requires much fewer reconstructed image slices, and help reduce the computational cost of both image reconstruction and training such models. To the best of our knowledge, this is the first report regarding Transfer Learning for 3D object recognition using integral imaging.