{'fraction_non_alphanumeric': 0.038280725319006045, 'fraction_numerical': 0.001343183344526528, 'mean_word_length': 5.1570247933884295, '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}}
Robust Two-stage Graph Convolutional Network for Face Clustering

Face clustering has been widely studied, due to its broad applications in academia and industry. Regarding researches on clustering, two vital parts are involved: one is the feature representation space of the image, and the other is the clustering algorithm. However, most of the current researches lay more emphasis on the latter and overlook the need for an appropriate feature representation space for clustering. Therefore, we propose a novel clustering framework, to address the problem of insufficiently compact features in clustering. Our method is comprised of a feature topology learning module (GCN-FT) and an auto-search clustering module (GCN-AS), which called GCN-F&A (GCN-FT & GCN-AS). Specifically, GCN-FT adds a self-adaptive learning structure to the traditional GCN to capture the internal correlation of features, so that the module can better aggregate information from itself and its neighbor nodes, and provide more gathered features for clustering. Our GCN-AS consists of two parts, one is ‘1-NN’, and the other is clustering module based on linkage prediction, namely ‘GCN-LP’. 1-NN is an effective pre-clustering method, which can automatically search hyper-parameters required in GCN-LP, thereby improving the scalability of the clustering module. Experiments on two large-scale face benchmarks and one clothing dataset demonstrate that our method significantly outperforms the state-of-the-arts.