ViT

This series explores DenseNet’s revolutionary approach to neural connectivity that solved vanishing gradients and improved feature reuse, examines its mathematical foundations and practical implementation, and discusses how its limitations eventually paved the way for Vision Transformers. We trace the evolution from convolutional networks to hybrid architectures, showing how each innovation built upon previous breakthroughs while addressing their shortcomings in the endless pursuit of more efficient and powerful deep learning models.

Vision Transformers (ViTs) are redefining image recognition by using Transformer models to capture global context, unlike traditional Convolutional Neural Networks (CNNs) that focus on local features. ViTs excel with large datasets and show impressive scalability and performance.