What is gradient checkpointing and why is it used in large vision models?

Gradient checkpointing trades compute for memory by recomputing activations during backpropagation.

Deep vision models like ViTs require large memory for storing activations. Checkpointing saves only selected intermediate activations and recomputes others during backward pass. This reduces memory usage significantly at the cost of extra computation.