the Diffusion Transformer showed the convolutional inductive bias is not necessary for high-quality generation; convolution survives as the VAE encoder and decoder, not as the generative backbone
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Why study this historical topic?
the Diffusion Transformer showed the convolutional inductive bias is not necessary for high-quality generation; convolution survives as the VAE encoder and decoder, not as the generative backbone
Convolutional Inductive Bias as a Requirement for Image Generation
the belief the rename of this curation exists to correct — locality and weight sharing were an efficiency shortcut, not a requirement
Why Transformers Replace CNNs in Computer Vision via Self-Attention and Multiscale Analysis
The theory posits that Self-Attention mechanisms in Transformer architectures act as a strict mathematical superset of Convolutional Neural Networks (CNNs), capable of expressing any local convolutio…