[Moving this question from applications to part1-v2 here]
I have collected some image data for classification tasks A and B. A pre-trained model (say ResNet, Inception-v3) has transfer-learned task A and am not happy with 50% accuracy, so I plan to loop in task B labels because I plan to learn B as well and transfer learning on task B training data does not help either task A or task B, which is when I stumbled upon Multi-task Learning. I am quoting from Han Xiao’s blogpost (link below), for a better description:
‘‘One interesting observation here is that the tasks are highly related: knowing the labels of one task could help one to guess the labels of another task. So perhaps it would be a good idea to train all those tasks in the same neural network simultaneously, hoping that the commonality across the tasks will be exploited during the learning and improve the final performance.’’
I have come across a few tutorials online:
Am I missing out on any other good CNN-centric-example to help me understand this?
What is the easiest way to get started? I strongly feel this can give us an edge in competitions such as Kaggle.