Using Convolutional Neural Network to create apparent age estimation model. Experiment with a number of methods to research their impact on the training process and different kid of biases.
The final report with the summary of contribution and the results discussion.
Load the dataset, and the ResNet50 model. Use transfer learning to adjust the model for the given task and perform fine-tuning.
Investigate the dataset with respect to the underlying biases. Implement simple methods like data augmentation and weighted samples to improve the training.
Experiment with complex custom solutions for reducing biases, like complex weights or custom loss functions.