Self-supervised learning (SSL) applies to vision transformers (ViTs) by allowing these models to learn useful feature representations from unlabeled data. In traditional supervised learning, models are trained on labeled datasets, which can be expensive and time-consuming to curate. SSL addresses this limitation by enabling ViTs to learn directly from the input images without the need for annotations. This is done through various techniques that help the model infer useful patterns and structures from the unlabelled data.
One common method of implementing SSL with ViTs is through the use of contrastive learning or masked image modeling. For example, in masked image modeling, parts of an image are intentionally hidden (masked), and the model is tasked with predicting the missing regions based on the visible parts. This encourages the ViT to learn rich representations of the entire image context. Another popular method is BYOL (Bootstrap Your Own Latent), where two augmentations of the same image are passed through two identical networks, and the model learns to predict one representation from the other. Such techniques are effective for ViTs, as they leverage the model's ability to capture long-range dependencies and complex relationships in the data.
Integrating SSL into ViTs not only helps improve their performance on downstream tasks but also makes the models more efficient regarding data requirements. By training on large amounts of unlabeled data, developers can harness the capabilities of ViTs without relying heavily on labeled datasets. This approach is particularly useful in fields like medical imaging or remote sensing, where labels are scarce. As a result, self-supervised learning enhances the flexibility and robustness of vision transformers, making them more applicable in various real-world scenarios.