  Transfer learning is a popular practice in deep neural networks, but
fine-tuning of large number of parameters is a hard task due to the complex
wiring of neurons between splitting layers and imbalance distributions of data
in pretrained and transferred domains. The reconstruction of the original
wiring for the target domain is a heavy burden due to the size of
interconnections across neurons. We propose a distributed scheme that tunes the
convolutional filters individually while backpropagates them jointly by means
of basic probability assignment. Some of the most recent advances in evidence
theory show that in a vast variety of the imbalanced regimes, optimizing of
some proper objective functions derived from contingency matrices prevents
biases towards high-prior class distributions. Therefore, the original filters
get gradually transferred based on individual contributions to overall
performance of the target domain. This largely reduces the expected complexity
of transfer learning whilst highly improves precision. Our experiments on
standard benchmarks and scenarios confirm the consistent improvement of our
distributed deep transfer learning strategy.
