GCN-Based Linkage Prediction for Face Clustering on Imbalanced Datasets: An Empirical Study

In recent years, benefiting from the expressive power of Graph Convolutional Networks (GCNs), significant breakthroughs have been made in face clustering. However, rare attention has been paid to GCN-based clustering on imbalanced data. Although imbalance problem has been extensively studied, the impact of imbalanced data on GCN-based linkage prediction task is quite different, which would cause problems in two aspects: imbalanced linkage labels and biased graph representations. The problem of imbalanced linkage labels is similar to that in image classification task, but the latter is a particular problem in GCN-based clustering via linkage prediction. Significantly biased graph representations in training can cause catastrophic overfitting of a GCN model. To tackle these problems, we evaluate the feasibility of those existing methods for imbalanced image classification problem on graphs with extensive experiments, and present a new method to alleviate the imbalanced labels and also augment graph representations using a Reverse-Imbalance Weighted Sampling (RIWS) strategy, followed with insightful analyses and discussions. The code and a series of imbalanced benchmark datasets synthesized from MS-Celeb-1M and DeepFashion are available on https://github.com/espectre/GCNs_on_imbalanced_datasets.

GCN-Based Linkage Prediction for Face Clusteringon Imbalanced Datasets: An Empirical Study

In recent years, benefiting from the expressivepower of Graph Convolutional Networks (GCNs),significant breakthroughs have been made in faceclustering. However, rare attention has been paidto GCN-based clustering on imbalanced data. Al-though imbalance problem has been extensivelystudied, the impact of imbalanced data on GCN-based linkage prediction task is quite different,which would cause problems in two aspects: im-balanced linkage labels and biased graph represen-tations. The problem of imbalanced linkage labelsis similar to that in image classification task, but thelatter is a particular problem in GCN-based clus-tering via linkage prediction. Significantly biasedgraph representations in training can cause catas-trophic overfitting of a GCN model. To tacklethese problems, we evaluate the feasibility of thoseexisting methods for imbalanced image classifica-tion problem on graphs with extensive experiments,and present a new method to alleviate the imbal-anced labels and also augment graph representa-tions using a Reverse-Imbalance Weighted Sam-pling (RIWS) strategy, followed with insightfulanalyses and discussions. A series of imbalancedbenchmark datasets synthesized from MS-Celeb-1M and DeepFashion will be openly available.