ConvMixer Model

ConvMixer is a relatively new, simple convolutional neural network architecture designed for image classification and related tasks, aiming to achieve high accuracy with fewer parameters than more complex models like Vision Transformers. Current research focuses on improving its privacy preservation through encryption techniques and enhancing its robustness against adversarial attacks, while also exploring variations like SplitMixer to further optimize efficiency and performance. These efforts are significant because they contribute to developing more efficient and secure deep learning models for resource-constrained environments and applications requiring data privacy.