{'fraction_non_alphanumeric': 0.014725568942436412, 'fraction_numerical': 0.0, 'mean_word_length': 6.123809523809523, '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}}
Continual Learning for Instance Segmentation to Mitigate Catastrophic Forgetting

In recent years, with the development of GPUs, there are various deep learning models that show excellent performance in the field of computer vision, but these models have a fatal limitation of catastrophic forgetting. Continual learning studies for mitigating the catastrophic forgetting have been conducted on classification tasks, but in real-life, object detection and segmentation tasks are more practical than classification tasks. This paper proposes a catastrophic forgetting solution in the instance segmentation network by extending and applying the catastrophic forgetting solution, which was previously limited to classification, to segmentation tasks.