Adaptive Random Feature Regularization on Fine-tuning Deep Neural
  Networks
Shin'ya Yamaguchi Sekitoshi Kanai Kazuki Adachi Daiki Chijiwa
While fine-tuning is a de facto standard method for training deep neural
networks, it still suffers from overfitting when using small target datasets.
Previous methods improve fine-tuning performance by maintaining knowledge of
the source datasets or introducing regularization terms such as contrastive
loss. However, these methods require auxiliary source information (e.g., source
labels or datasets) or heavy additional computations. In this paper, we propose
a simple method called adaptive random feature regularization (AdaRand).
AdaRand helps the feature extractors of training models to adaptively change
the distribution of feature vectors for downstream classification tasks without
auxiliary source information and with reasonable computation costs. To this
end, AdaRand minimizes the gap between feature vectors and random reference
vectors that are sampled from class conditional Gaussian distributions.
Furthermore, AdaRand dynamically updates the conditional distribution to follow
the currently updated feature extractors and balance the distance between
classes in feature spaces. Our experiments show that AdaRand outperforms the
other fine-tuning regularization, which requires auxiliary source information
and heavy computation costs.