Top-tuning: A study on transfer learning for an efficient alternative to fine tuning for image classification with fast kernel methods
IMAGE AND VISION COMPUTING(2024)
摘要
The impressive performance of deep learning architectures is associated with a massive increase in model complexity. Millions of parameters need to be tuned, with training and inference time scaling accordingly, together with energy consumption. But is massive fine-tuning always necessary? In this paper, focusing on image classification, we consider a simple transfer learning approach exploiting pre-trained convolutional features as input for a fast-to-train kernel method. We refer to this approach as top-tuning since only the kernel classifier is trained on the target dataset. In our study, we perform more than 3000 training processes focusing on 32 small to medium-sized target datasets, a typical situation where transfer learning is necessary. We show that the toptuning approach provides comparable accuracy with respect to fine-tuning, with a training time between one and two orders of magnitude smaller. These results suggest that top-tuning is an effective alternative to finetuning in small/medium datasets, being especially useful when training time efficiency and computational resources saving are crucial.
更多查看译文
关键词
Fast kernel methods,Training on a budget,Fast training,Transfer learning,Image classification
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要