How do Vision Transformers work? – Paper explained | multi-head self-attention & convolutions

It turns out that multi-head self-attention and convolutions are complementary. So, what makes multi-head self-attention different from convolutions? How and why do Vision Transformers work? In this video, we will find out by explaining the paper “How Do Vision Transformers Work?” by Namuk & Kim, 2021.

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⏩ Vision Transformers explained playlist: https://youtube.com/playlist?list=PLpZBeKTZRGPMddKHcsJAOIghV8MwzwQV6
📺 ViT: An image is worth 16x16 pixels: https://youtu.be/DVoHvmww2lQ
📺 Swin Transformer: https://youtu.be/SndHALawoag
📺 ConvNext: https://youtu.be/QqejV0LNDHA
📺 DeiT: https://youtu.be/-FbV2KgRM8A
📺 Adversarial attacks: https://youtu.be/YyTyWGUUhmo

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Paper 📜:
Park, Namuk, and Songkuk Kim. "How Do Vision Transformers Work?." In International Conference on Learning Representations. 2021. https://openreview.net/forum?id=D78Go4hVcxO
🔗 Official implementation: https://github.com/xxxnell/how-do-vits-work

Outline:
00:00 Transformers vs ConvNets
01:04 Sponsor: Weights & Biases
02:21 Convolutions explained in a nutshell
03:35 Multi-Head Self-Attention explained
06:46 Why we thought that MSA is cool
09:56 Paper insights
15:26 MSA vs. Convs (more insight)
16:07 Low-pass filters (MSA) and high-pass filters (Convs)

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Видео How do Vision Transformers work? – Paper explained | multi-head self-attention & convolutions канала AI Coffee Break with Letitia