Neural network architectures with multiple normalization layers for machine vision

Abstract

One example aspect of the present disclosure is directed to a neural network for machine vision. The neural network may include a stem block that includes a set of stem layers. The neural network may additionally include a visual transformer block. The set of stem layers may include a patch layer, a first normalization layer, an embedding layer, and a second normalization layer. The patch layer subdivides an input image into a set of image patches. The first normalization layer generates a set of normalized image patches by performing a first normalization process on each image patch of the set of image patches. The patch layer feeds forward to the first normalization layer. The embedding layer generates a set of vector embeddings. Each vector embedding of the set of embedding vectors is a projection of a corresponding normalized image patch from the set of normalized image patches onto a visual token. The first normalization layer feeds forward to the embedding layer. The second normalization layer generates a set of normalized vector embeddings by performing a second normalization process on each vector embedding of the set of vector embeddings. The embedding layer feeds forward to the second normalization layer. The transformer block enables one or more machine vision tasks for the input image based on the set of normalized vectors. The second normalization layer feeds forward to the transformer block.