Summary of Vision Transformer-based Adversarial Domain Adaptation, by Yahan Li et al.
Vision Transformer-based Adversarial Domain Adaptation
by Yahan Li, Yuan Wu
First submitted to arxiv on: 24 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper explores the application of vision transformer (ViT) in unsupervised domain adaptation (UDA). The primary goal is to transfer knowledge from a labeled source domain to an unlabeled target domain. By employing ViT as the feature extractor, the authors demonstrate its effectiveness in adversarial domain adaptation and show that it can be a plug-and-play component in existing UDA methods, leading to performance improvements. The paper investigates the potential of using ViT-based feature extractors in place of convolutional neural networks (CNNs) for various computer vision tasks such as image classification, object detection, and semantic segmentation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about using a new type of artificial intelligence called vision transformer (ViT) to help computers learn from one kind of data but apply it to another kind. This is important because computers often struggle to understand different types of data without being taught first. The researchers used ViT as the “brain” of a computer program that helps with this problem and found that it works really well. They also showed that this new brain can replace an old one (called CNN) in many cases, making the process more efficient. |
Keywords
» Artificial intelligence » Cnn » Domain adaptation » Image classification » Object detection » Semantic segmentation » Unsupervised » Vision transformer » Vit
