Self-supervised learning (SSL) trains neural networks by generating labels directly from the input data instead of relying on external annotations. The core idea is to create a “pretext task” where parts of the data are hidden, transformed, or used to predict other parts, forcing the model to learn meaningful representations. For example, in text, a sentence might have words masked, and the model learns to predict the missing words. The network adjusts its parameters through backpropagation to minimize the error in solving these synthetic tasks, effectively learning patterns and structures inherent in the data. This approach leverages the abundance of unlabeled data, making it practical for domains where labeled data is scarce or expensive to obtain.
A common example is masked language modeling, used in models like BERT. Here, 15% of words in a sentence are randomly replaced with a [MASK] token, and the model predicts the original words. This requires understanding context, syntax, and semantics. For images, a pretext task might involve predicting the relative positions of cropped patches or reconstructing missing portions of an image (inpainting). Contrastive learning is another SSL technique: the model learns to identify whether two augmented views (e.g., rotated, cropped) belong to the same original image. By pulling similar data points closer in embedding space and pushing dissimilar ones apart, the network builds robust feature representations. These tasks are designed to ensure the model captures generalizable features rather than memorizing specifics.
The main advantage of SSL is reduced dependency on labeled data, which is especially valuable for domains like medical imaging or multilingual NLP where annotations are limited. However, SSL requires careful design of pretext tasks to ensure they align with downstream applications. For instance, a model trained to predict image rotations may not perform well on object detection if rotation prediction doesn’t emphasize spatial relationships. Additionally, SSL often demands significant computational resources for pre-training, though fine-tuning on labeled data later is typically faster. Developers can implement SSL using frameworks like PyTorch or TensorFlow by defining custom loss functions and data augmentation pipelines, balancing task complexity with computational efficiency.