Multimodal-Guided Dynamic Dataset Pruning for Robust and Efficient Data-Centric Learning
This work addresses data redundancy and quality issues in deep learning for practitioners seeking more efficient and robust training methods, though it appears incremental as it builds on existing data-centric approaches.
The paper tackles the problem of dataset pruning for efficient deep learning by introducing a dynamic framework that adaptively selects training samples based on task difficulty and cross-modality consistency, using pretrained multimodal models to filter uninformative data.
Modern deep models are trained on large real-world datasets, where data quality varies and redundancy is common. Data-centric approaches such as dataset pruning have shown promise in improving training efficiency and model performance. However, most existing methods rely on static heuristics or task-specific metrics, limiting their robustness and generalizability across domains. In this work, we introduce a dynamic dataset pruning framework that adaptively selects training samples based on both task-driven difficulty and cross-modality semantic consistency. By incorporating supervision from pretrained multimodal foundation models, our approach captures training dynamics while effectively filtering out uninformative samples. Our work highlights the potential of integrating cross-modality alignment for robust sample selection, advancing data-centric learning toward more efficient and robust practices across application domains.