Deep Learning Face Attributes in the Wild

@article{Liu2015DeepLF,
  title={Deep Learning Face Attributes in the Wild},
  author={Ziwei Liu and Ping Luo and Xiaogang Wang and Xiaoou Tang},
  journal={2015 IEEE International Conference on Computer Vision (ICCV)},
  year={2015},
  pages={3730-3738}
}
Predicting face attributes in the wild is challenging due to complex face variations. [...] Key Result (3) It also demonstrates that the high-level hidden neurons of ANet automatically discover semantic concepts after pre-training with massive face identities, and such concepts are significantly enriched after fine-tuning with attribute tags. Each attribute can be well explained with a sparse linear combination of these concepts.Expand
Face Attribute Prediction Using Off-The-Shelf Deep Learning Networks
Face Attribute Prediction with classification CNN
Face attribute prediction using off-the-shelf CNN features
Face Attributes Recognition via Deep Multi-Task Cascade
Leveraging mid-level deep representations for predicting face attributes in the wild
General-to-specific learning for facial attribute classification in the wild
Fine-Grained Face Annotation Using Deep Multi-Task CNN
Robust RGB-D Face Recognition Using Attribute-Aware Loss
Transferring from face recognition to face attribute prediction through adaptive selection of off-the-shelf CNN representations
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