{'fraction_non_alphanumeric': 0.03007518796992481, 'fraction_numerical': 0.0, 'mean_word_length': 5.563380281690141, '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}}
Reducing Image Compression Artifacts for Deep Neural Networks

Existing compression artifacts reduction methods aim to restore images on pixel-level, which can improve the human visual experience. However, in many applications, large-scale images are collected not for visual examination by humans. Instead, they are used for many high-level vision tasks usually by Deep Neural Networks (DNN). In this paper, we find that these methods have limited performance improvements to high-level tasks, even bring negative effects. Therefore, inspired by the teacher-student network framework, we propose a compression artifacts reduction framework (ARF) for DNN. In addition, we generalize our method to the unsupervised setting (U-ARF) where the corresponding original images are unavailable in training. Extensive experiments indicate the proposed methods can help DNNs improve performance on the highly compressed images significantly.