arxiv preprint - MLP-Mixer: An all-MLP Architecture for Vision

In this episode we discuss MLP-Mixer: An all-MLP Architecture for Vision by Ilya Tolstikhin, Neil Houlsby, Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Thomas Unterthiner, Jessica Yung, Andreas Steiner, Daniel Keysers, Jakob Uszkoreit, Mario Lucic, Alexey Dosovitskiy. The paper presents MLP-Mixer, an architecture that relies solely on multi-layer perceptrons (MLPs) for image classification tasks, demonstrating that neither convolutions nor attention mechanisms are necessary for high performance. The MLP-Mixer operates with two types of layers: one that processes features within individual image patches, and another that blends features across different patches. The model achieves competitive results on benchmarks when trained on large datasets or with modern regularization techniques, suggesting a new direction for image recognition research beyond conventional CNNs and Transformers.