Yearbook Age Prediction (CS395T)

نویسنده

  • Darshan Thaker
چکیده

We tackle the yearbook ’Face To Age’ challenge to predict the year that a given yearbook photo was taken in. We experiment with three variants of the VGG19 architecture [4], the AlexNet architecture [2], as well as different loss functions and an extensive effort to reduce overfitting. A variant of the AlexNet architecture, that has 2048 neurons in the fully connected layers and is ensembled over 5 models, gives us the best results, with a mean L1 norm distance of 5.54.

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تاریخ انتشار 2017