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.

该论文提出了一种针对预训练的 3D 点云模型的鲁棒微调方法，以增强下游微调模型中的特征鲁棒性。通过权重空间集成和线性探测相结合的方法，该方法显著提高了下游微调模型在分布变化情况下的性能，同时在目标分布上保持高性能，以提高特征鲁棒性。将这种鲁棒微调方法应用于主流的 3D 点云预训练模型，并评估模型参数的质量和下游任务性能的降低。实验结果表明，WiSE-FT-LP 方法有效提升了模型的鲁棒性，有效平衡了下游任务性能和模型特征的鲁棒性，而不改变模型结构。

预训练的 3D 点云模型的稳健微调

Robust Fine-tuning for Pre-trained 3D Point Cloud Models

Batch normalization (BN) has been widely used in modern deep neural networks
(DNNs) due to improved convergence. BN is observed to increase the model
accuracy while at the cost of adversarial robustness. There is an increasing
interest in the ML community to understand the impact of BN on DNNs, especially
related to the model robustness. This work attempts to understand the impact of
BN on DNNs from a non-robust feature perspective. Straightforwardly, the
improved accuracy can be attributed to the better utilization of useful
features. It remains unclear whether BN mainly favors learning robust features
(RFs) or non-robust features (NRFs). Our work presents empirical evidence that
supports that BN shifts a model towards being more dependent on NRFs. To
facilitate the analysis of such a feature robustness shift, we propose a
framework for disentangling robust usefulness into robustness and usefulness.
Extensive analysis under the proposed framework yields valuable insight on the
DNN behavior regarding robustness, e.g. DNNs first mainly learn RFs and then
NRFs. The insight that RFs transfer better than NRFs, further inspires simple
techniques to strengthen transfer-based black-box attacks.

本研究分析批量标准化在深度神经网络中的影响，提出通过分离鲁棒性和有效性来评估其对神经网络的贡献，并探究其对于特征鲁棒性的贡献程度以及其对于黑盒攻击的可重用性。

批量归一化增加对抗性漏洞性并降低对抗性可传递性：一个非强健特征的视角

Batch Normalization Increases Adversarial Vulnerability and Decreases  Adversarial Transferability: A Non-Robust Feature Perspective

Flatness of the loss curve is conjectured to be connected to the
generalization ability of machine learning models, in particular neural
networks. While it has been empirically observed that flatness measures
consistently correlate strongly with generalization, it is still an open
theoretical problem why and under which circumstances flatness is connected to
generalization, in particular in light of reparameterizations that change
certain flatness measures but leave generalization unchanged. We investigate
the connection between flatness and generalization by relating it to the
interpolation from representative data, deriving notions of representativeness,
and feature robustness. The notions allow us to rigorously connect flatness and
generalization and to identify conditions under which the connection holds.
Moreover, they give rise to a novel, but natural relative flatness measure that
correlates strongly with generalization, simplifies to ridge regression for
ordinary least squares, and solves the reparameterization issue.

本文研究了损失曲线的平直性与机器学习模型的泛化能力之间的联系，通过推导出代表性数据的概念，并提出新的相对平直性的度量方法，解决了薄板回归等复杂度量方法所带来的重新参数化的问题。