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| 약지도 학습 비전 트랜스포머× | 준지도 학습× | |
|---|---|---|
| 분야≠ | 딥러닝 | 머신러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2021–2022 | 1970s–2006 (formalized) |
| 창시자≠ | Dosovitskiy et al. (ViT); weak supervision paradigm from Zhou and others | Vapnik, V. N. and others (community of researchers, 1970s–2000s) |
| 유형≠ | Self-attention image model with weakly supervised training | Learning paradigm |
| 원전≠ | 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). link ↗ | Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9 |
| 별칭 | WS-ViT, weakly supervised ViT, weak supervision with vision transformer, ViT with weak labels | SSL, semi-supervised machine learning, transductive learning, label-efficient learning |
| 관련≠ | 4 | 5 |
| 요약≠ | 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. | Semi-supervised learning (SSL) is a machine learning paradigm that trains models using a small set of labeled examples together with a much larger pool of unlabeled data. By leveraging the structure inherent in unlabeled data, SSL achieves accuracy closer to fully supervised models while requiring far fewer costly manual labels — making it practical when labeling is expensive, slow, or resource-constrained. |
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