The pretraining-finetuning paradigm has gained popularity in various computer
vision tasks. In this paradigm, the emergence of active finetuning arises due
to the abundance of large-scale data and costly annotation requirements. Active
finetuning involves selecting a subset of data from an unlabeled pool for
annotation, facilitating subsequent finetuning. However, the use of a limited
number of training samples can lead to a biased distribution, potentially
resulting in model overfitting. In this paper, we propose a new method called
ActiveDC for the active finetuning tasks. Firstly, we select samples for
annotation by optimizing the distribution similarity between the subset to be
selected and the entire unlabeled pool in continuous space. Secondly, we
calibrate the distribution of the selected samples by exploiting implicit
category information in the unlabeled pool. The feature visualization provides
an intuitive sense of the effectiveness of our approach to distribution
calibration. We conducted extensive experiments on three image classification
datasets with different sampling ratios. The results indicate that ActiveDC
consistently outperforms the baseline performance in all image classification
tasks. The improvement is particularly significant when the sampling ratio is
low, with performance gains of up to 10%. Our code will be released.

提出了一种名为 ActiveDC 的新方法，通过优化子集与整个未标记样本池之间的分布相似性和利用未标记样本池中的隐式类别信息，选择样本进行注释并校准其分布，从而在所有图像分类任务中持续优于基准性能，特别是在采样比率较低时，获得高达 10％的性能提升。