  Fashion attribute classification is of great importance to many high-level
tasks such as fashion item search, fashion trend analysis, fashion
recommendation, etc. The task is challenging due to the extremely imbalanced
data distribution, particularly the attributes with only a few positive
samples. In this paper, we introduce a hard-aware pipeline to make full use of
"hard" samples/attributes. We first propose Hard-Aware BackPropagation (HABP)
to efficiently and adaptively focus on training "hard" data. Then for the
identified hard labels, we propose to synthesize more complementary samples for
training. To stabilize training, we extend semi-supervised GAN by directly
deactivating outputs for synthetic complementary samples (Deact). In general,
our method is more effective in addressing "hard" cases. HABP weights more on
"hard" samples. For "hard" attributes with insufficient training data, Deact
brings more stable synthetic samples for training and further improve the
performance. Our method is verified on large scale fashion dataset,
outperforming other state-of-the-art without any additional supervisions.
