  Image Retrieval grows to be an integral part of fashion e-commerce ecosystem
as it keeps expanding in multitudes. Other than the retrieval of visually
similar items, the retrieval of visually compatible or complementary items is
also an important aspect of it. Normal Siamese Networks tend to work well on
complementary items retrieval. But it fails to identify low level style
features which make items compatible in human eyes. These low level style
features are captured to a large extent in techniques used in neural style
transfer. This paper proposes a mechanism of utilising those methods in this
retrieval task and capturing the low level style features through a hybrid
siamese network coupled with a hybrid loss. The experimental results indicate
that the proposed method outperforms traditional siamese networks in retrieval
tasks for complementary items.
