In this article, the authors explore the potential of automated data augmentation (ADA) to improve transfer learning (TL) in computer vision tasks. They propose a novel approach called Hybrid Transformer-based Autoaugmentation (HTAA), which combines the strengths of two existing techniques: transformer-based models and autoaugmentation.
Background: Transfer learning has become a crucial component in many machine learning applications, particularly in computer vision tasks. However, it faces challenges due to the limited availability of target data, leading to degraded attention quality in pre-trained models. Autoaugmentation, which involves generating new training data from existing data, has shown promise in addressing this issue. Nevertheless, most autoaugmentation methods suffer from two limitations: (1) they only focus on non-object regions, and (2) they fail to utilize well-trained representations transferred from pre-trained models.
Proposed Approach: HTAA addresses these limitations by hybridizing transformer-based models with autoaugmentation. The proposed approach leverages the strong inductive bias of transformer-based models to focus on object-centric regions and utilizes well-trained representations from pre-trained models to improve attention quality. By combining these two techniques, HTAA can effectively augment the target data, leading to improved TL performance.
Experiments: The authors conducted extensive experiments on several benchmark datasets, including CIFAR-10, CIFAR-100, and ImageNet. They evaluated the performance of HTAA against various baselines and showed that it outperforms existing autoaugmentation methods in most cases. Moreover, they demonstrated that HTAA can adapt to different dataset sizes and characteristics by adjusting a single hyperparameter, λ.
Key Findings: The key findings of this study can be summarized as follows:
- Hybridizing transformer-based models with autoaugmentation leads to improved TL performance.
- HTAA effectively focuses on object-centric regions and utilizes well-trained representations from pre-trained models to improve attention quality.
- The optimal value of λ varies depending on the characteristics of the dataset, and it is important to adjust this hyperparameter for each dataset.
Conclusion: In conclusion, HTAA offers a promising solution for improving TL performance in computer vision tasks by leveraging the strengths of transformer-based models and autoaugmentation. The proposed approach has broad applications in various TL scenarios and can be easily integrated into existing TL pipelines. By adapting to different dataset sizes and characteristics, HTAA can provide a more robust and efficient TL framework for a wide range of computer vision tasks.
