Questions tagged [multi-task-learning]

For questions related to multi-task learning (MTL), which is a subfield of machine learning in which multiple learning tasks are solved at the same time, while exploiting commonalities and differences across tasks. MTL can result in improved learning efficiency and prediction accuracy for the task-specific models, when compared to training the models separately.

Filter by
Sorted by
Tagged with
0
votes
1answer
24 views

How should I incorporate numerical and categorical data as part of the inputs to the U-net for semantic segmentation?

I am using a U-Net to segment cancer cells in images of patients' arms. I would like to add patient data to it in order to see if it is possible to enhance the segmentation (patient data comes in the ...
1
vote
0answers
19 views

How to deal with losses on different scales in multi-task learning?

Say I'm training a model for multiple tasks by trying to minimize sum of losses $L_1 + L_2$ via gradient descent. If these losses are on a different scale, the one whose range is greater will dominate ...
0
votes
0answers
18 views

Multi-objective training vs Transfer learning - pros and cons

I'm solving a sequential prediction task that has multiple features attached to each timestamp, and my goal is to predict one of them. However, the feature's label is highly imbalanced: only about 4% ...
1
vote
0answers
16 views

How do I format task features with a one-hot task identification vector to ensure separate weight matrices for each task in multi-task RL?

I am on Lecture 2 of Stanford CS330 Multi-Task and Meta-learning, and on slide 10, the professor describes using a one-hot input vector to represent the task, and she also explained that there would ...