Learning concepts that are consistent with human perception is important for
Deep Neural Networks to win end-user trust. Post-hoc interpretation methods
lack transparency in the feature representations learned by the models. This
work proposes a guided learning approach with an additional concept layer in a
CNN- based architecture to learn the associations between visual features and
word phrases. We design an objective function that optimizes both prediction
accuracy and semantics of the learned feature representations. Experiment
results demonstrate that the proposed model can learn concepts that are
consistent with human perception and their corresponding contributions to the
model decision without compromising accuracy. Further, these learned concepts
are transferable to new classes of objects that have similar concepts.

该论文提出了一种带有附加概念层的 CNN 架构的引导学习方法，用于学习视觉特征和单词短语之间的关联，并通过优化预测准确性和特征表示的语义来学习与人类感知一致的概念，实验结果表明，所提出的模型可以在不牺牲准确性的情况下学习一致于人类感知的概念，并可将这些学习到的概念转移到具有相似概念的新对象类别中。