SSL (self-supervised learning) models differ from traditional deep learning models primarily in how they handle training data and learn representations. Traditional deep learning typically relies on supervised learning, where models are trained on labeled datasets—each input example (e.g., an image) is paired with a corresponding target output (e.g., a class label). SSL models, however, generate their own supervisory signals from unlabeled data. For example, a common SSL approach involves masking parts of an input (like hiding words in a sentence or patches in an image) and training the model to predict the missing parts. This eliminates the need for manually annotated labels, making SSL scalable for large datasets where labeling is expensive or impractical.
A key structural difference lies in the training process. Traditional models often follow a single-stage training pipeline where labeled data is used end-to-end to optimize task-specific objectives (e.g., classification loss). SSL models, by contrast, often use a two-stage approach: pre-training and fine-tuning. During pre-training, the model learns general-purpose features by solving a pretext task (e.g., predicting image rotations or reconstructing corrupted text). These learned features are then fine-tuned on a smaller labeled dataset for a specific downstream task (e.g., sentiment analysis). For instance, BERT, a popular SSL model for NLP, is pre-trained on masked language modeling and next-sentence prediction tasks before being adapted to tasks like question answering.
Another distinction is the efficiency of feature learning. SSL models excel at capturing rich, transferable representations because they are exposed to diverse, uncurated data during pre-training. Traditional models, especially when trained on limited labeled data, may struggle to generalize beyond the specific patterns in their training set. For example, a self-supervised vision model like MoCo (Momentum Contrast) learns to distinguish between image augmentations (e.g., cropped or rotated versions of the same image), enabling it to recognize visual patterns robustly. In contrast, a traditional CNN trained on a small labeled dataset might overfit to superficial features. SSL’s reliance on inherent data structure rather than explicit labels makes it particularly effective in domains like medical imaging or multilingual NLP, where labeled data is scarce but raw data is abundant.