3 papers
cs.LG2026
When Shared Knowledge Hurts: Spectral Over-Accumulation in Model Merging
Yayuan Li, Ze Peng, Jian Zhang +3
Model merging combines multiple fine-tuned models into a single model by adding their weight updates, providing a lightweight alternative to retraining. Existing methods primarily…
cs.LG2025
MAGIC: Achieving Superior Model Merging via Magnitude Calibration
Yayuan Li, Jian Zhang, Jintao Guo +4
The proliferation of pre-trained models has given rise to a wide array of specialised, fine-tuned models. Model merging aims to merge the distinct capabilities of these specialised…
cs.CV2024
Text and Image Are Mutually Beneficial: Enhancing Training-Free Few-Shot Classification with CLIP
Yayuan Li, Jintao Guo, Lei Qi +2
Contrastive Language-Image Pretraining (CLIP) has been widely used in vision tasks. Notably, CLIP has demonstrated promising performance in few-shot learning (FSL). However, existi…