- [D] How to integrate ViT into U-Net using this library? https://github.com/lucidrains/vit-pytorch#vision-transformer-for-small-datasets 3 comments machinelearning
Linking pages
- GitHub - ml-tooling/best-of-ml-python: 🏆 A ranked list of awesome machine learning Python libraries. Updated weekly. https://github.com/ml-tooling/best-of-ml-python 0 comments
- GitHub - xxxnell/how-do-vits-work: (ICLR 2022 Spotlight) Official PyTorch implementation of "How Do Vision Transformers Work?" https://github.com/xxxnell/how-do-vits-work 0 comments
- Papers with Code 2021 : A Year in Review | by elvis | PapersWithCode | Medium https://medium.com/paperswithcode/papers-with-code-2021-a-year-in-review-de75d5a77b8b 0 comments
Linked pages
- Transformers from scratch | peterbloem.nl http://peterbloem.nl/blog/transformers 40 comments
- GitHub - lucidrains/x-transformers: A simple but complete full-attention transformer with a set of promising experimental features from various papers https://github.com/lucidrains/x-transformers 37 comments
- The Illustrated Transformer – Jay Alammar – Visualizing machine learning one concept at a time. https://jalammar.github.io/illustrated-transformer/ 20 comments
- [2103.14030] Swin Transformer: Hierarchical Vision Transformer using Shifted Windows https://arxiv.org/abs/2103.14030 20 comments
- [2103.15691] ViViT: A Video Vision Transformer https://arxiv.org/abs/2103.15691 4 comments
- The Annotated Transformer https://nlp.seas.harvard.edu/2018/04/03/attention.html 3 comments
- [2105.12723] Nested Hierarchical Transformer: Towards Accurate, Data-Efficient and Interpretable Visual Understanding https://arxiv.org/abs/2105.12723 3 comments
- [2104.14294] Emerging Properties in Self-Supervised Vision Transformers https://arxiv.org/abs/2104.14294 3 comments
- [2111.06377] Masked Autoencoders Are Scalable Vision Learners https://arxiv.org/abs/2111.06377 2 comments
- [2102.03902] Nyströmformer: A Nyström-Based Algorithm for Approximating Self-Attention https://arxiv.org/abs/2102.03902 2 comments
- [2103.17239] Going deeper with Image Transformers https://arxiv.org/abs/2103.17239 1 comment
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