Reference-Guided Clustering for Efficient Training
Figure 1 : Illustration of our reference‐guided clustering and efficiency gains. (a) Direct clustering on the full unlabeled dataset is computationally expensive. (b) Our reference‐guided strategy selects a representative, scene-aware subset around reference centroids. (c) Compared with full-dataset clustering, our method achieves much lower FID at substantially reduced cost time. And compared with best baseline, ours requires 1.9× to 2.1× fewer training iterations across different pruning ratios.
论文上下文
Paper title: RS-Prune: Training-Free Data Pruning at High Ratios for Efficient Remote Sensing Diffusion Foundation Models Abstract: Diffusion-based remote sensing (RS) generative foundation models are cruial for downstream tasks. However, these models rely on large amounts of globally representative data, which often contain redundancy, noise, and class imbalance, reducing training efficiency and preventing convergence. Existing RS diffusion foundation models typically aggregate multiple classification datasets or apply simplistic deduplication, overlooking the distributional requirements of generation modeling and the heterogeneity of RS imagery. To address these limitations, we propose a training-free, two-stage data pruning approach that quickly select a high-quality subset under high pruning ratios, enabling a preliminary foundation model to converge rapidly and serve as a versatile backbone for generation, downstream fine-tuning, and other applications. Our method jointly considers local information content with global scene-level diversity and representativeness. First, an entropy-based criterion efficiently removes low-information samples. Next, leveraging RS scene classification datasets as reference benchmarks, we perform scene-aware clustering with stratified sampling to improve clustering effectiveness while reducing computational costs on large-scale unlabeled data. Finally, by balancing cluster-level uniformity and sample representativeness, the method enables fine-grained selection under high pruning ratios while preserving overall diversity and representativeness. Experiments show that, eve Passages referencing this figure: Figure 1 : Illustration of our reference‐guided clustering and efficiency gains.
