  Deep neural networks require a large amount of labeled training data during
supervised learning. However, collecting and labeling so much data might be
infeasible in many cases. In this paper, we introduce a source-target selective
joint fine-tuning scheme for improving the performance of deep learning tasks
with insufficient training data. In this scheme, a target learning task with
insufficient training data is carried out simultaneously with another source
learning task with abundant training data. However, the source learning task
does not use all existing training data. Our core idea is to identify and use a
subset of training images from the original source learning task whose
low-level characteristics are similar to those from the target learning task,
and jointly fine-tune shared convolutional layers for both tasks. Specifically,
we compute descriptors from linear or nonlinear filter bank responses on
training images from both tasks, and use such descriptors to search for a
desired subset of training samples for the source learning task.
  Experiments demonstrate that our selective joint fine-tuning scheme achieves
state-of-the-art performance on multiple visual classification tasks with
insufficient training data for deep learning. Such tasks include Caltech 256,
MIT Indoor 67, Oxford Flowers 102 and Stanford Dogs 120. In comparison to
fine-tuning without a source domain, the proposed method can improve the
classification accuracy by 2% - 10% using a single model.
