  Fine-tuning is widely applied in image classification tasks as a transfer
learning approach. It re-uses the knowledge from a source task to learn and
obtain a high performance in target tasks. Fine-tuning is able to alleviate the
challenge of insufficient training data and expensive labelling of new data.
However, standard fine-tuning has limited performance in complex data
distributions. To address this issue, we propose the Adaptable Multi-tuning
method, which adaptively determines each data sample's fine-tuning strategy. In
this framework, multiple fine-tuning settings and one policy network are
defined. The policy network in Adaptable Multi-tuning can dynamically adjust to
an optimal weighting to feed different samples into models that are trained
using different fine-tuning strategies. Our method outperforms the standard
fine-tuning approach by 1.69%, 2.79% on the datasets FGVC-Aircraft, and
Describable Texture, yielding comparable performance on the datasets Stanford
Cars, CIFAR-10, and Fashion-MNIST.
