Title: Transformed CNNs: recasting pre-trained convolutional layers with self-attention. Abstract: Vision Transformers (ViT) have recently emerged as a powerful alternative to convolutional networks (CNNs).  Although hybrid models attempt to bridge the gap between these two architectures, the self-attention layers they rely on induce a strong computational bottleneck, especially at large spatial resolutions. In this work, we explore the idea of reducing the time spent training these layers by initializing them from pre-trained convolutional layers. This enables us to transition smoothly from any pre-trained CNN to its functionally identical hybrid model, called Transformed CNN (T-CNN). With only 50 epochs of fine-tuning, the resulting T-CNNs demonstrate significant performance gains over the CNN as well as substantially improved robustness. We analyze the representations learnt by theT-CNN, providing deeper insights into the fruitful interplay between convolutions and self-attention.