Adaptive Depth

Adaptive depth techniques aim to optimize computational efficiency in various machine learning models by dynamically adjusting the network's depth based on input characteristics. Current research focuses on developing methods to efficiently determine which layers to skip or adjust depth parameters (e.g., using self-distillation, hypernetworks, or multi-armed bandits) within architectures like transformers and convolutional neural networks. This approach improves performance-efficiency trade-offs across diverse applications, including image recognition, speech processing, and 3D scene reconstruction, by reducing computational cost without significant accuracy loss. The resulting models are more resource-efficient and adaptable to varying computational constraints.