![]() ![]() We first create an ensemble anchor-based pairwise similarity matrix to enhance the robustness of similarity and dissimilarity relations between training samples and anchors. To alleviate this issue, in this paper, we propose a novel unsupervised deep pairwise hashing method to effectively and robustly exploit the similarity information between training samples and multiple anchors. Additionally, its performance might be significantly improved by effectively exploiting the pair similarity relationship among training data, but the attained similarity matrix usually contains noisy information, which often largely decreases the model performance. ![]() Although unsupervised deep hashing is potentially very useful for tackling many large-scale tasks, its performance is still far below satisfactory. ![]()
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