  This paper presents a robust fine-tuning method designed for pre-trained 3D
point cloud models, to enhance feature robustness in downstream fine-tuned
models. We highlight the limitations of current fine-tuning methods and the
challenges of learning robust models. The proposed method, named Weight-Space
Ensembles for Fine-Tuning then Linear Probing (WiSE-FT-LP), integrates the
original pre-training and fine-tuning models through weight space integration
followed by Linear Probing. This approach significantly enhances the
performance of downstream fine-tuned models under distribution shifts,
improving feature robustness while maintaining high performance on the target
distribution. We apply this robust fine-tuning method to mainstream 3D point
cloud pre-trained models and evaluate the quality of model parameters and the
degradation of downstream task performance. Experimental results demonstrate
the effectiveness of WiSE-FT-LP in enhancing model robustness, effectively
balancing downstream task performance and model feature robustness without
altering the model structures.
