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Age progression\/regression by conditional adversarial autoencoder .In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2017:5810- 5818 . Zhang Z, SongY, Qi H.Age progression\/regression by conditional adversarial autoencoder.In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017:5810- 5818 ."},{"volume-title":"2017 IEEE International Conference on Image Processing (ICIP)","author":"Antipov G","key":"e_1_3_2_1_39_1","unstructured":"Antipov G , Baccouche M , Dugelay J. Face aging with conditional generative adversarial networks . In: 2017 IEEE International Conference on Image Processing (ICIP) , 2017: 2089-2093. Antipov G, Baccouche M, Dugelay J.Face aging with conditional generative adversarial networks. In: 2017 IEEE International Conference on Image Processing (ICIP), 2017: 2089-2093."},{"volume-title":"Proceedings of the IEEE International Conference on Computer Vision(ICCV)","author":"Duong C","key":"e_1_3_2_1_40_1","unstructured":"Duong C , Quach K , Luu K , Temporal non-volume preserving approach to facial age-progression and age-invariant face recognition .In: Proceedings of the IEEE International Conference on Computer Vision(ICCV) , 2017:3755\u20133763. Duong C, Quach K , Luu K, Temporal non-volume preserving approach to facial age-progression and age-invariant face recognition.In: Proceedings of the IEEE International Conference on Computer Vision(ICCV), 2017:3755\u20133763."},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00828"},{"volume-title":"IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","author":"Yang H","key":"e_1_3_2_1_42_1","unstructured":"Yang H , Huang D , Wang Y , Learning face age progression: a pyramid architecture of GANs . In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2018:31\u201339. Yang H, Huang D, Wang Y, Learning face age progression: a pyramid architecture of GANs. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018:31\u201339."},{"volume-title":"IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","author":"Liu Y","key":"e_1_3_2_1_43_1","unstructured":"Liu Y , Li Q , Sun Z. Attribute-aware face aging with wavelet-based generative adversarial networks . In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2019:11877\u201311886. Liu Y, Li Q, Sun Z.Attribute-aware face aging with wavelet-based generative adversarial networks. 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