Bootstrap Deep Spectral Clustering with Optimal Transport

--Spectral clustering is a leading clustering method. Two of its major shortcomings are the disjoint optimization process and the limited representation capacity. T o address these issues, we propose a deep spectral clustering model (named BootSC), which jointly learns all stages of spectral clustering-- affinity matrix construction, spectral embedding, and k -means clustering--using a single network in an end-to-end manner . Moreover, a semantically-consistent orthogonal re-parameterization technique is introduced to or-thogonalize spectral embeddings, significantly enhancing the discrimination capability. Experimental results indicate that BootSC achieves state-of-the-art clustering performance. For example, it accomplishes a notable 16% NMI improvement over the runner-up method on the challenging ImageNet-Dogs dataset. EEP clustering models aim to detect underlying cluster structures within unlabelled data. To train these models, creating effective and efficient supervision signals is necessary. Inadequate supervision could result in excessive computational costs [1], training instability [2], and degenerate results [3]. Classical deep clustering models [5], [6], [7], [8], [9], [10] commonly adopt cluster assignments obtained by k -means on data representations as training supervision. A major challenge with this k -means-style supervision is that data representations are assumed to follow simple isotropic Gaussian distributions.