Multitask learning (MTL) plays a significant role in self-supervised learning (SSL) by enabling models to learn multiple objectives simultaneously, which improves their ability to generalize. In SSL, models are trained on unlabeled data using pretext tasks—like predicting missing parts of an input or contrasting similar and dissimilar samples. MTL enhances this by combining multiple pretext tasks, forcing the model to capture richer, more robust representations. For example, a model might learn to predict image rotations and reconstruct masked patches at the same time. This approach reduces the risk of the model overfitting to a single task and encourages the discovery of features useful across diverse scenarios.
From a technical perspective, MTL in SSL often involves designing a shared encoder that processes input data, with task-specific heads for each pretext objective. The encoder learns to extract features that satisfy all tasks, while the heads specialize in converting those features into task-specific outputs. For instance, in natural language processing, a transformer model might be trained to predict masked tokens (like BERT) and reorder shuffled sentences. The shared layers learn syntactic and semantic patterns that serve both tasks, leading to more versatile embeddings. In computer vision, a convolutional network could simultaneously solve jigsaw puzzles and colorize grayscale images, with the encoder capturing spatial hierarchies and texture details relevant to both. Developers can implement this using frameworks like PyTorch by computing separate losses for each task and combining them (e.g., summing weighted losses) during backpropagation.
The benefits of MTL in SSL include improved data efficiency and better downstream task performance. For example, a model trained on multiple pretext tasks might require fewer labeled examples for fine-tuning on tasks like classification or segmentation. However, challenges include balancing task contributions (e.g., avoiding one task dominating the loss) and selecting compatible tasks. Techniques like uncertainty weighting or gradient normalization can help manage conflicting gradients. A practical application is in medical imaging, where labeled data is scarce: a model trained to predict MRI scan rotations and inpaint missing regions could learn features useful for diagnosing multiple conditions. By carefully designing task combinations, developers can create SSL models that generalize better and adapt to real-world scenarios with limited supervision.