  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.
