A New Method To Code Inductive Image Biases Into Models Using CNN And Transformers

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Researchers at Heidelberg University have recently proposed a novel method to efficiently code inductive image biases into models while retaining all transformers’ flexibility. This approach combines the inductive bias’s effectiveness in convolutional neural networks (CNNs) with transformers’ expressivity to model and synthesize high-resolution images.

Transformer’s Limitations

Transformers have shown promising results in learning long-range interactions on sequential data and have been employed for language tasks and increasingly adapted to reinforcement learning, audio, and computer vision.

Read full paper summary: https://www.marktechpost.com/2020/12/28/a-new-method-to-code-inductive-image-biases-into-models-using-cnn-and-transformers/

Paper: https://arxiv.org/pdf/2012.09841.pdf

Github: https://compvis.github.io/taming-transformers/

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