Generative models and self-supervised learning are closely connected because generative models are often used as a core component of self-supervised learning frameworks. Self-supervised learning (SSL) is a training paradigm where models learn from unlabeled data by generating their own supervision signals, typically by predicting parts of the input from other parts. Generative models, which focus on modeling the underlying data distribution to create new samples, naturally align with this goal. For example, training a model to predict missing words in a sentence or reconstruct corrupted image pixels requires understanding the structure of the data, which is a generative task. This relationship allows SSL to leverage generative models to learn meaningful representations without relying on labeled datasets.
A key example of this connection is seen in natural language processing (NLP). Models like BERT use masked language modeling—a self-supervised task where the model predicts randomly masked words in a sentence. This is inherently generative because the model must produce plausible words to fill in the blanks. Similarly, autoregressive models like GPT generate text by predicting the next token in a sequence, which is both a generative and self-supervised objective. In computer vision, generative models such as variational autoencoders (VAEs) or denoising autoencoders are trained to reconstruct corrupted or incomplete images, another form of self-supervised learning. These tasks force the model to learn robust features by solving synthetic but meaningful prediction problems derived from the data itself.
The synergy between generative models and SSL offers practical advantages. First, it enables the use of vast amounts of unlabeled data, which is cheaper and more abundant than labeled data. For instance, pretraining a generative model on SSL tasks like image inpainting or text prediction can produce a general-purpose feature extractor, which is then fine-tuned on smaller labeled datasets for specific tasks like classification. Second, generative SSL tasks encourage the model to capture the data’s underlying structure, improving generalization. While not all SSL methods are generative (e.g., contrastive learning uses discriminative objectives), generative approaches remain a dominant strategy in SSL due to their effectiveness in representation learning. This relationship continues to drive advancements in domains like NLP, computer vision, and multimodal AI.