  Active learning is to design label-efficient algorithms by sampling the most
representative samples to be labeled by an oracle. In this paper, we propose a
state relabeling adversarial active learning model (SRAAL), that leverages both
the annotation and the labeled/unlabeled state information for deriving the
most informative unlabeled samples. The SRAAL consists of a representation
generator and a state discriminator. The generator uses the complementary
annotation information with traditional reconstruction information to generate
the unified representation of samples, which embeds the semantic into the whole
data representation. Then, we design an online uncertainty indicator in the
discriminator, which endues unlabeled samples with different importance. As a
result, we can select the most informative samples based on the discriminator's
predicted state. We also design an algorithm to initialize the labeled pool,
which makes subsequent sampling more efficient. The experiments conducted on
various datasets show that our model outperforms the previous state-of-art
active learning methods and our initially sampling algorithm achieves better
performance.
