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Self-supervised Vision Transformer

Self-supervised Vision Transformer (SSL-ViT) applies self-supervised pre-training objectives — such as masked patch prediction (MAE) or self-distillation with no labels (DINO) — to the Vision Transformer architecture, enabling powerful visual representations to be learned from large unlabeled image corpora before any task-specific fine-tuning.

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Sources

  1. Caron, M., Touvron, H., Misra, I., Jegou, H., Mairal, J., Bojanowski, P., & Joulin, A. (2021). Emerging Properties in Self-Supervised Vision Transformers. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 9650–9660. link ↗
  2. He, K., Chen, X., Xie, S., Li, Y., Dollar, P., & Girshick, R. (2022). Masked Autoencoders Are Scalable Vision Learners. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 16000–16009. link ↗

Related methods

Fine-Tuned Vision TransformerMultimodal Vision TransformerSelf-supervised convolutional neural networkVision Transformer

Referenced by

Explainable Vision TransformerSelf-supervised convolutional neural networkSelf-supervised Semantic SegmentationSemi-supervised Instance SegmentationSemi-supervised Vision Transformer

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ScholarGate — Self-supervised Vision Transformer (Self-supervised Vision Transformer (SSL-ViT)). Retrieved 2026-06-04 from https://scholargate.app/tr/deep-learning/self-supervised-vision-transformer