Contextually Affinitive Neighborhood Refinery for Deep Clustering
This work addresses the challenge of improving clustering accuracy in self-supervised learning, though it is incremental as it builds on existing frameworks.
The paper tackles the problem of limited supervision signals in deep clustering by proposing an online re-ranking process to mine informative neighbors and a boundary filtering strategy to reduce noise, achieving state-of-the-art performance on popular benchmarks.
Previous endeavors in self-supervised learning have enlightened the research of deep clustering from an instance discrimination perspective. Built upon this foundation, recent studies further highlight the importance of grouping semantically similar instances. One effective method to achieve this is by promoting the semantic structure preserved by neighborhood consistency. However, the samples in the local neighborhood may be limited due to their close proximity to each other, which may not provide substantial and diverse supervision signals. Inspired by the versatile re-ranking methods in the context of image retrieval, we propose to employ an efficient online re-ranking process to mine more informative neighbors in a Contextually Affinitive (ConAff) Neighborhood, and then encourage the cross-view neighborhood consistency. To further mitigate the intrinsic neighborhood noises near cluster boundaries, we propose a progressively relaxed boundary filtering strategy to circumvent the issues brought by noisy neighbors. Our method can be easily integrated into the generic self-supervised frameworks and outperforms the state-of-the-art methods on several popular benchmarks.