GSEC uses MLLM-generated semantic descriptions and a bi-layer ensemble (BatchEnsemble inner layer plus alignment outer layer) to reduce bias and variance, outperforming 18 prior methods on six image clustering benchmarks.
A decision-theoretic generalization of on-line learning and an application to boosting.Journal of com- puter and system sciences, 55(1):119–139
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Reducing Bias and Variance: Generative Semantic Guidance and Bi-Layer Ensemble for Image Clustering
GSEC uses MLLM-generated semantic descriptions and a bi-layer ensemble (BatchEnsemble inner layer plus alignment outer layer) to reduce bias and variance, outperforming 18 prior methods on six image clustering benchmarks.