Depthwise scaling

Topic

Depthwise scaling is a model scaling technique in deep learning that increases the capacity of a neural network by adding more layers (increasing its depth). It is commonly used to scale up architectures like convolutional neural networks and large language models, allowing them to learn more complex, hierarchical features.