How does SSL apply to vision transformers (ViTs)?

Self-supervised learning (SSL) enables vision transformers (ViTs) to learn meaningful image representations without relying on manually labeled data. SSL achieves this by creating pre-training tasks that leverage the inherent structure of images. For ViTs, this typically involves dividing images into patches (e.g., 16x16 pixel grids) and training the model to solve tasks like reconstructing masked patches or contrasting similar/dissimilar image views. These tasks force the model to learn spatial and semantic relationships between patches, building a foundational understanding of visual data. For example, a ViT might process a sequence of patches where 75% are randomly masked, then predict the missing pixel values or patch embeddings—a technique inspired by masked language modeling in NLP.

A common SSL approach for ViTs is masked autoencoding, exemplified by methods like MAE (Masked Autoencoder). Here, the model learns to reconstruct masked image patches using a transformer encoder-decoder architecture. The encoder processes visible patches, while the decoder reconstructs the full image from the encoder’s output and mask tokens. Another approach is contrastive learning, where the model learns to identify whether two augmented views (e.g., cropped, rotated, or color-adjusted versions) originate from the same image. For instance, a ViT trained with DINO (a self-distillation method) compares global and local patch-level features across augmented views to create consistent representations. These methods exploit the transformer’s ability to model long-range dependencies between patches, making SSL particularly effective for ViTs compared to convolutional networks.

The benefits of SSL for ViTs include reduced dependency on labeled datasets and improved generalization for downstream tasks like classification or segmentation. However, challenges remain. Training ViTs with SSL requires significant computational resources due to the quadratic complexity of self-attention over patches. Additionally, designing effective pre-training tasks that capture diverse visual patterns is critical. For example, masking too few patches might make the task trivial, while excessive masking could limit the model’s ability to learn meaningful context. Despite these challenges, SSL has become a standard approach for pre-training ViTs, as seen in frameworks like Timm or Hugging Face’s implementations, where pre-trained SSL weights are often fine-tuned for specific applications with minimal labeled data.