Face-Aging-CAAE-Pytorch

• Pytorch implementation of Age Progression/Regression by Conditional Adversarial Autoencoder
• reference: TensorFlow implementation of CAAE
• gave a presentation in 2017 YONSEI BIGDATA CONFERENCE by team FACEBIGTA.

Requirements

• pytorch 0.2.0
• UTKFace Aligned&Cropped dataset

Usage

• git clone or download zip file of this repository
• download Aligned & Cropped version of UTKFace from here
• execute main.py in bash

python main.py

Results

UTKFace

rows: years of 0 ~ 5, 5 ~ 10, 10 ~ 15, 16 ~ 20, 21 ~ 30, 31 ~ 40, 41 ~ 50, 51 ~ 60, 61 ~ 70, over 70

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