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DeepCluster is a self-supervision approach for learning image representations. DeepCluster iteratively groups the features with a standard clustering algorithm, k-means, and uses the subsequent assignments as supervision to update the weights of the network
Deep Clustering for Unsupervised Learning of Visual Features
Permutation Invariant Training of Deep Models for Speaker-Independent Multi-talker Speech Separation
Improving k-Means Clustering Performance with Disentangled Internal Representations
Structurally Regularized Deep Clustering, or SRDC, is a deep network based discriminative clustering method for domain adaptation that minimizes the KL divergence between predictive label distribution of the network and an introduced auxiliary one. Replacing the auxiliary distribution with that formed by ground-truth labels of source data implements the structural source regularization via a simple strategy of joint network training.
Unsupervised Domain Adaptation via Structurally Regularized Deep Clustering
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.