CDWF achieves 90-99% of full fine-tuning performance with up to 120x fewer trainable parameters by dynamically allocating full trainability to gradient-important blocks and LoRA to others for PV cyberattack transfer learning.
Data-driven cyber- attack detection for pv farms via time-frequency domain features
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.NE 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Constraint-Driven Warm-Freeze for Efficient Transfer Learning in Photovoltaic Systems
CDWF achieves 90-99% of full fine-tuning performance with up to 120x fewer trainable parameters by dynamically allocating full trainability to gradient-important blocks and LoRA to others for PV cyberattack transfer learning.