💦🏸What is multi-task machine learning?
Usually one ML-model solves one problem, for example, image classification or text synthesis. This is called single-task learning (STL). But some models allow you to make several types of predictions on one sample, for example, image classification and semantic segmentation. This is already multitasking training (MTL, Multi-task learning). The main advantages of MTL are as follows:
• smaller training sample for each of the individual tasks due to the enlargement of the total data set;
• improved generalization of the model - information from related tasks increases the ability of the model to extract useful data from the dataset and reduces overfitting;
• shortening the training duration - instead of wasting time training multiple models to solve multiple problems, a single model is trained;
• reduced requirements for hardware resources - ML-models have many parameters that need to be stored in RAM. Therefore, for devices with limited computing power, IoT, it is better to have one MTL with some common parameters, rather than several STL models that perform a number of related tasks.
The downside to these benefits is performance degradation. During MTL training, tasks can compete with each other. For example, when instance segmentation (segmentation of a separate mask for each distinct object in an image) is trained along with semantic segmentation (classification of objects at the pixel level), the latter task often dominates unless a task balancing mechanism is used.
In addition, the MTL loss function is more complex as a result of summing the individual losses, making optimization difficult. This is where the so-called negative transmission effect occurs when performing multiple tasks, and separate STL models can perform better than a single MTL.
Looking ahead, multitasking machine learning is great for natural language processing and medical research, but the current implementation of this approach does not fully cover its current drawbacks.
https://thegradient.pub/how-to-do-multi-task-learning-intelligently/