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Given a pre-trained CNN without any testing samples, this paper proposes asimple yet effective method to diagnose feature representations of the CNN. Weaim to discover representation flaws caused by potential dataset bias. Morespecifically, when the CNN is trained to estimate image attributes, we minelatent relationships between representations of different attributes inside theCNN. Then, we compare the mined attribute relationships with ground-truthattribute relationships to discover the CNN's blind spots and failure modes dueto dataset bias. In fact, representation flaws caused by dataset bias cannot beexamined by conventional evaluation strategies based on testing images, becausetesting images may also have a similar bias. Experiments have demonstrated theeffectiveness of our method.

Examining CNN Representations with respect to Dataset Bias