Enhancing Robustness of Vision-Language Models through Orthogonality Learning and Self-Regularization

TL;DR

This paper proposes an orthogonal fine-tuning method with self-regularization for vision-language models like CLIP, enhancing robustness, stability, and generalization, especially in few-shot scenarios, by injecting orthogonal matrices and employing data augmentation.

Contribution

Introduces OrthSR, a novel orthogonal fine-tuning approach with self-regularization, improving stability and generalization of VLMs during task-specific adaptation.

Findings

Enhanced zero-shot generalization with self-regularization.

Improved few-shot classification performance.

Faster convergence and stability during fine-tuning.

Abstract

Efficient fine-tuning of vision-language models (VLMs) like CLIP for specific downstream tasks is gaining significant attention. Previous works primarily focus on prompt learning to adapt the CLIP into a variety of downstream tasks, however, suffering from task overfitting when fine-tuned on a small data set. In this paper, we introduce an orthogonal fine-tuning method for efficiently fine-tuning pretrained weights and enabling enhanced robustness and generalization, while a self-regularization strategy is further exploited to maintain the stability in terms of zero-shot generalization of VLMs, dubbed OrthSR. Specifically, trainable orthogonal matrices are injected seamlessly into the transformer architecture and enforced with orthogonality constraint during the training, benefiting from the norm-preserving property and thus leading to stable and faster convergence, while keeping the pre-trained weights frozen. To alleviate deviation from fine-tuning, a self-regularization strategy is further employed to retain the generalization of the model during the training within a bypass manner. In addition, to enrich the sample diversity for downstream tasks under the small dataset scenario, we first explore attentive CutOut data augmentation to boost the efficient fine-tuning, leading to better model fitting capacity for specific downstream task. Then we support the theoretical analysis on how our approach improves the specific downstream performance and maintains the generalizability. For the first time, we revisit the CLIP and CoOp with our method to effectively improve the model on few-shot image classficiation scenario on par with the elaborated prompt learning methods.