Shot Compression

Shot compression focuses on efficiently reducing the size and computational cost of large neural networks, particularly using limited training data. Current research emphasizes techniques like block-level pruning and attention module elimination within Vision Transformers (ViTs) and Convolutional Neural Networks (CNNs), often employing strategies such as feature mimicking to avoid overfitting with small datasets. These advancements aim to improve the efficiency and accessibility of deep learning models, enabling deployment on resource-constrained devices and addressing data privacy concerns by reducing the need for extensive training data.