Structural Re Parameterization

Structural re-parameterization (Rep) is a deep learning technique that modifies neural network architectures during training to improve performance without increasing inference costs. Current research focuses on applying Rep to various architectures, including convolutional neural networks (CNNs) like RepVGG, Vision Transformers (ViTs), and even optimizers themselves, often addressing challenges like quantization and efficient adaptation to downstream tasks. This approach offers significant advantages in deploying efficient and accurate models for various applications, from image classification and object detection to medical image analysis and biological image processing, by streamlining model complexity without sacrificing accuracy.