Overview of Residual Multi-Layer Perceptrons (ResMLP)
Residual Multi-Layer Perceptrons, or ResMLP for short, is a type of architecture used for image classification. ResMLP is built entirely on multi-layer perceptrons, which are algorithms used in machine learning to create artificial neural networks that learn from data input. The ResMLP architecture is a simple residual network that alternates a linear layer and a feed-forward network in which channels interact independently per patch.
The ResMLP architecture consists of two main components, namely linear layers and feed-forward networks. In the linear layer, image patches interact independently and identically across channels. In contrast, the feed-forward network allows channels to interact independently per patch. At the end of the network, the patch representations are average pooled and fed to a linear classifier.
Layer Normalization in ResMLP
In ResMLP, layer normalization is replaced with a simpler process called an affine transformation. This is thanks to the absence of self-attention layers that make training more stable than using layer normalization. The affine operator is applied at the beginning (pre-normalization) and end (post-normalization) of each residual block. As a pre-normalization step, the affine transformation replaces layer normalization without using channel-wise statistics. The initialization is achieved as alpha = 1 and beta = 0. As a post-normalization step, the affine transformation is similar to LayerScale, and alpha is initialized with the same small value.
The Benefits of Using ResMLP
ResMLP has many benefits that make it an attractive option for image classification. One of the advantages of using ResMLP is its simplicity. The architecture is easier to understand and implement than some of the more complex neural network models. Another advantage is its efficiency. Due to the simplicity of the ResMLP model, it has a smaller number of parameters, leading to faster training times and lower computational requirements.
ResMLP also has a higher accuracy rate than some of the more complex neural network models, such as Convolutional Neural Networks (CNNs). This is because ResMLP is better at modeling long-range dependencies in images, leading to a better understanding of the image's overall structure.
In summary, Residual Multi-Layer Perceptrons (ResMLP) is a simple yet efficient architecture used for image classification, built entirely on multi-layer perceptrons. ResMLP replaces layer normalization with an affine transformation, making training more stable. ResMLP is a popular model for image classification due to its simplicity, efficiency, and high accuracy rate. ResMLP is an excellent option for those looking to start with neural networks or those who want a more efficient and accurate model than the commonly used Convolutional Neural Networks.
