ConViT: improving vision transformers with soft convolutional inductive biases*

Abstract Convolutional architectures have proven to be extremely successful for vision tasks. We conclude by presenting various ablations to better understand the success of the ConViT.

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• 1 Their hard inductive biases enable sample-efficient learning, but come at the cost of a potentially lower performance ceiling.
• 2 Vision transformers rely on more flexible self-attention layers, and have recently outperformed CNNs for image classification.
• 3 However, they require costly pre-training on large external datasets or distillation from pre-trained convolutional networks.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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