13 citations · 25 across the 14 of their papers we have counts for
4 papers · 1 filter
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…
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…
On the Implicit Adversariality of Catastrophic Forgetting in Deep Continual Learning
Ze Peng, Jian Zhang, Jintao Guo +3
Continual learning seeks the human-like ability to accumulate new skills in machine intelligence. Its central challenge is catastrophic forgetting, whose underlying cause has not b…
PLACE dropout: A Progressive Layer-wise and Channel-wise Dropout for Domain Generalization
Jintao Guo, Lei Qi, Yinghuan Shi +1
Domain generalization (DG) aims to learn a generic model from multiple observed source domains that generalizes well to arbitrary unseen target domains without further training. Th…