  This paper presents a self-adaptive training (SAT) model for fashion
compatibility prediction. It focuses on the learning of some hard items, such
as those that share similar color, texture, and pattern features but are
considered incompatible due to the aesthetics or temporal shifts. Specifically,
we first design a method to define hard outfits and a difficulty score (DS) is
defined and assigned to each outfit based on the difficulty in recommending an
item for it. Then, we propose a self-adaptive triplet loss (SATL), where the DS
of the outfit is considered. Finally, we propose a very simple conditional
similarity network combining the proposed SATL to achieve the learning of hard
items in the fashion compatibility prediction. Experiments on the publicly
available Polyvore Outfits and Polyvore Outfits-D datasets demonstrate our
SAT's effectiveness in fashion compatibility prediction. Besides, our SATL can
be easily extended to other conditional similarity networks to improve their
performance.
