قارن الطرق

	شبكات الخصومة التوليدية ذاتية الإشراف	محوّل الرؤية
المجال	التعلم العميق	التعلم العميق
العائلة	Machine learning	Machine learning
سنة النشأة	2019	2021
صاحب الطريقة	Chen, T., Zhai, X., Ritter, M., Lucic, M., & Houlsby, N.	Dosovitskiy, A. et al.
النوع	Generative model with self-supervised auxiliary tasks	Transformer architecture for images (self-attention over patches)
المصدر التأسيسي	Chen, T., Zhai, X., Ritter, M., Lucic, M., & Houlsby, N. (2019). Self-Supervised GANs via Auxiliary Rotation Loss. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 12154–12163.	Dosovitskiy, A. et al. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ICLR.
الأسماء البديلة	SS-GAN, Self-supervised GAN, Self-supervised Generative Adversarial Network, GAN with self-supervised auxiliary tasks	Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images
ذات صلة	5	5
الملخص	Self-supervised GAN augments a standard Generative Adversarial Network with one or more self-supervised auxiliary tasks — such as predicting image rotation or patch position — that stabilise adversarial training and yield a discriminator that learns rich, transferable representations from unlabeled data without requiring manual annotations.	The Vision Transformer (ViT), introduced by Dosovitskiy and colleagues in 2021, splits an image into fixed-size patches, treats those patches as a sequence, and applies the Transformer self-attention mechanism to image classification. Given enough training data, it surpasses convolutional neural networks (CNNs).