ResMLP: Feedforward Networks for Image Classification With Data-Efficient Training.

Area of Science:

• Computer Science
• Artificial Intelligence
• Machine Learning

Background:

• Convolutional Neural Networks (CNNs) dominate image classification.
• Exploring alternative architectures like MLPs is crucial for advancing deep learning.

Purpose of the Study:

• Introduce ResMLP, a novel image classification architecture based entirely on MLPs.
• Evaluate ResMLP's performance with modern training strategies and self-supervised learning.
• Assess ResMLP's adaptability to other domains like machine translation.

Main Methods:

• ResMLP architecture alternates patch interaction (linear layer) and channel interaction (feed-forward network).
• Employed modern training strategies including heavy data-augmentation and distillation.
• Trained models in a self-supervised setup to minimize dataset priors.
• Adapted the model architecture for machine translation tasks.

Main Results:

• ResMLP achieves competitive accuracy/complexity trade-offs on ImageNet.
• Self-supervised training further enhances ResMLP's performance without labeled data.
• The architecture demonstrates surprising effectiveness in machine translation.

Conclusions:

• ResMLP presents a viable and efficient MLP-based alternative for image classification.
• The architecture's flexibility extends to other domains, showing promise beyond vision tasks.
• Availability of pre-trained models and code facilitates further research and application.