![]() Given an image divided into patches, MLP-Mixer uses an initial linear layer to generate 1,024 representations of each patch. ![]() The authors pre-trained MLP-Mixer for image classification using ImageNet-21k, which contains 21,000 classes, and refined it on ImageNet which has 1,000 classes. How was MLP-Mixer designed and how does it work? This work shows that they can compete with the most powerful architectures for image classification. It should be noted that MLPs are the simplest “building blocks” of deep learning. Their creation was published in a paper entitled “MLP-Mixer: An all-MLP Architecture for Vision”. So they designed MLP-Mixer, which allows MLPs to exploit this process. Ilya Tolstikhin, Neil Houlsby, Alexander Kolesnikov and Lucas Beyer, along with Google Brain researchers, came up with the idea of modifying MLPs so that they could process and compare images through patches rather than by analyzing each pixel individually. MLPs do not have this bias, so they tend to take into account interpixel relationships that exist, but are not necessary to the image processing process. In the future, it is quite possible that the simplest multilayer neural networks could be more sophisticated than the most advanced current architectures.Ī study to exploit multilayer perceptrons for image classification and computer visionĬurrently, convolutional neural networks excel in image processing and computer vision because they are designed to discern spatial relationships, and pixels that are close together in an image tend to be more related than pixels that are far apart. This is a no-frills model that approaches state-of-the-art performance in ImageNet classification, and could achieve performance comparable to systems like ViT (Vision Transformer), BiT (Big Transfer), HaloNet and NF-Net. A research team at Google Brain has revisited multilayer perceptrons (MLP) by designing MLP-Mixer.
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