Adapting Convolutional Filters for Improved Visual Task Performance

Fine-tuning is a common technique used in deep learning to adapt pre-trained models to new tasks. However, fine-tuning can be computationally expensive and time-consuming, especially when dealing with large models. In this article, the authors propose a novel approach called "delta-tuning" that significantly reduces the computational cost of fine-tuning while maintaining its efficiency.
Delta-tuning works by applying small adjustments to the pre-trained model’s weights instead of retraining the entire model. These adjustments are made using a simple nonlinear function, which helps the model adapt to new tasks more quickly and with less computational effort. The authors demonstrate that delta-tuning achieves comparable performance to full fine-tuning on several classification datasets while reducing the computational cost by up to 90%.
To understand how delta-tuning works, imagine a pre-trained model as a skilled chef who has mastered various cooking techniques. When faced with a new recipe, the chef can simply adjust their existing skills and experience to create a delicious dish without having to learn an entirely new set of cooking techniques. Similarly, delta-tuning adjusts the pre-trained model’s weights to adapt to new tasks without requiring extensive retraining.
The authors also explore how different filter scales in convolutional neural networks (CNNs) impact the performance of fine-tuning. They find that using multiple filter scales can help the model capture features at different levels of abstraction, leading to better performance on classification tasks.
In summary, delta-tuning is a computationally efficient approach to fine-tune pre-trained models for new tasks. By applying small adjustments to the weights instead of retraining the entire model, delta-tuning can significantly reduce the computational cost while maintaining performance. The authors demonstrate the effectiveness of delta-tuning on several classification datasets and show how different filter scales in CNNs impact fine-tuning performance. This work has important implications for deep learning practitioners who want to adapt pre-trained models to new tasks without incurring excessive computational costs.