Depth Limit

Depth limit research explores the challenges and limitations of increasing the depth (number of layers) in neural networks, focusing on mitigating issues like exploding gradients and ensuring stable signal propagation. Current research investigates architectural modifications, such as skip connections and specific normalization techniques, to improve training stability and achieve consistent performance across varying depths and widths. These advancements are crucial for improving the performance and scalability of deep learning models, impacting diverse applications from autonomous navigation (using depth estimation for traversability) to computer vision (improving self-supervised monocular depth reconstruction).