Multi Task Learning example for shape type, color, position detection

Implementation of a simple multi-task learner with 3 tasks (heads). Shares internal representation between different tasks (hard parameter sharing, see [2]) having the direct benefit of a lighter, faster model with less trainable parameters.

This implementation has ~192 k trainable parameters, compared to ~290 k used for the same tasks with a multi-headed implementation on separate branches, see [3]. Compared to [3] training samples and epochs are significantly increased, after 2k cycles position accuracy is ~84% while color and shape ~99%.

Steps:

1. Create toy database, generate shapes, color it
2. Normalize the generated images / output values (for the regression head)
3. Create the multi-task learner Keras functional model, train and test it
4. Generate the metrics:
   • mean_absolute_error - regression head
   • confusion matrix, f1-score for classification heads
5. (optional) Freeze the feature detetor part, finetune the regression head
6. reivew the metrics

Model:

Outputs:

1. Regression head (position)

1.1. Regression head (position), after fine-tuning:

2. Color prediction head Confusion Matrix

3. Shape type prediction head Confusion Matrix

Links

1. keras
2. An Overview of Multi-Task Learning in Deep Neural Networks, arXiv:1706.05098 [cs.LG]
3. Multi headed DNN predictor, detects object coordinates, color and shape type

/Enjoy.