Multi-task learning (MTL) in deep learning trains a single neural network to perform multiple related tasks simultaneously. Instead of training separate models for each task, MTL shares representations across tasks during training. This approach leverages shared features and patterns in the data, which can improve generalization and reduce overfitting. For example, a model trained to recognize objects in images might simultaneously predict object labels and segment object boundaries. By sharing layers early in the network and branching into task-specific layers later, the model learns a common feature representation that benefits all tasks.
A practical example of MTL is in natural language processing (NLP). A single model might be trained to perform named entity recognition (identifying names in text) and part-of-speech tagging (labeling nouns, verbs, etc.) at the same time. The shared layers learn general linguistic patterns, like syntax and grammar, while task-specific layers focus on their individual objectives. Another example is in self-driving car systems, where a model might predict lane boundaries, detect pedestrians, and estimate distances concurrently. These tasks share low-level visual features (edges, textures) but require distinct high-level outputs. The loss function in MTL typically combines losses from all tasks, often weighted to balance their influence during training.
The benefits of MTL include improved data efficiency (especially for tasks with limited data) and reduced computational overhead compared to training separate models. However, challenges arise in balancing task priorities. For instance, if one task’s loss dominates others, the model may underperform on less prominent tasks. Techniques like dynamic loss weighting (e.g., uncertainty-based weighting) or gradient normalization can mitigate this. Additionally, task conflicts—where learning one task harms another—can occur if tasks are unrelated. Careful architecture design (e.g., how layers are shared) and task selection are critical. When applied effectively, MTL can produce compact, versatile models that handle complex real-world scenarios efficiently.