DeepViT: Letting Vision Transformers Go Deeper
Some image models, called vision transformers, stops getting better when you add more layers. The reason? Their internal focus maps — the so called attention — start to look the same, layer after layer, so deeper parts just repeat what earlier parts already know. So more layers won’t help, and training becomes wasteful. A simple idea fix it. By gently re-making the focus maps at each stage, a trick named Re-attention keep them fresh and different with almost no extra cost. It lets the model learn new things in higher layers instead of repeating old ones. Results shows deeper models then give deeper understanding of images and better scores on big tests. This means that, with a small change, image models can actually…
DeepViT: Letting Vision Transformers Go Deeper
Some image models, called vision transformers, stops getting better when you add more layers. The reason? Their internal focus maps — the so called attention — start to look the same, layer after layer, so deeper parts just repeat what earlier parts already know. So more layers won’t help, and training becomes wasteful. A simple idea fix it. By gently re-making the focus maps at each stage, a trick named Re-attention keep them fresh and different with almost no extra cost. It lets the model learn new things in higher layers instead of repeating old ones. Results shows deeper models then give deeper understanding of images and better scores on big tests. This means that, with a small change, image models can actually profit from being tall again. No big redesign needed, only a tiny tweak to the usual code so many teams can try it fast. If you like pictures, apps may soon spot objects more clearly and make smarter choices for you.
Read article comprehensive review in Paperium.net: DeepViT: Towards Deeper Vision Transformer
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