Sammenlign metoder

	Svakt veiledet Vision Transformer	Vision Transformer
Fagfelt	Dyp læring	Dyp læring
Familie	Machine learning	Machine learning
Opprinnelsesår	2021–2022	2021
Opphavsperson	Dosovitskiy et al. (ViT); weak supervision paradigm from Zhou and others	Dosovitskiy, A. et al.
Type	Self-attention image model with weakly supervised training	Transformer architecture for images (self-attention over patches)
Opprinnelig kilde	Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., & Houlsby, N. (2021). An image is worth 16x16 words: Transformers for image recognition at scale. In International Conference on Learning Representations (ICLR).	Dosovitskiy, A. et al. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ICLR.
Alias	WS-ViT, weakly supervised ViT, weak supervision with vision transformer, ViT with weak labels	Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images
Relaterte	4	5
Sammendrag	Weakly Supervised Vision Transformer (WS-ViT) trains a Vision Transformer on image data that lacks precise pixel-level annotations, instead using cheaper, noisier supervision such as image-level class tags, bounding boxes, or web-scraped text. The global self-attention mechanism of the transformer makes it especially capable of localising objects and learning discriminative features from these incomplete labels.	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).