  Given a pre-trained CNN without any testing samples, this paper proposes a
simple yet effective method to diagnose feature representations of the CNN. We
aim to discover representation flaws caused by potential dataset bias. More
specifically, when the CNN is trained to estimate image attributes, we mine
latent relationships between representations of different attributes inside the
CNN. Then, we compare the mined attribute relationships with ground-truth
attribute relationships to discover the CNN's blind spots and failure modes due
to dataset bias. In fact, representation flaws caused by dataset bias cannot be
examined by conventional evaluation strategies based on testing images, because
testing images may also have a similar bias. Experiments have demonstrated the
effectiveness of our method.
