A Weakly Supervised Learning Framework Integrating Uncertainty-Aware and Class-Specific Attention for Non-Intrusive Load Monitoring
Non-intrusive load monitoring (NILM) is essential for fine-grained energy management, but its practical deployment is hindered by the high cost of acquiring fine-grained strong labels and cross-domain distribution shifts. To address this limitation, we propose UACA-Net, a hybrid weakly supervised framework for load dis...