Generative Adversarial Network (GAN) has been widely used for object attribute editing. However, the semantic correlation, resulted from the feature map interaction in the generative network of GAN, may impair the generalization ability of the generative network. In this work, semantic disentanglement is introduced in GAN to reduce the attribute correlation. The feature maps of the generative network are first grouped with an efficient clustering algorithm based on hash encoding, which are used to excavate hidden semantic attributes and calculate
the group-wise orthogonality loss for the reduction of attribute entanglement.
Meanwhile, the feature maps falling in the intersection regions of different groups are further suppressed to reduce the attribute-wise interaction.
Extensive experiments reveal that the proposed GAN generated more genuine objects than the state of the arts. Quantitative results of classification accuracy, inception score and FID score further justify the effectiveness of the proposed GAN.

Group-wise Feature Orthogonalization and Suppression for GAN based Facial Attribute Translation


PS T1.3: Deep Learning and Pattern Analysis

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ICPR 2020 is the premier international conference on Pattern Recognition, gathering leading researchers from around the world to present their latest achievements in Pattern Analysis and Recognition, applied Artificial Intelligence and Machine Learning. The topic of the year is " Putting Artificial intelligence to work on Patterns". 
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Zhiwei Wen

1

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Group-wise Feature Orthogonalization and Suppression for GAN based Facial Attribute Translation

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