A charcoal-kiln camp stacks a very deep stills-and-notes network. In one recipe the error signal becomes tiny after a few layers; in another the numbers explode. What failure modes are those?
Select an answer to reveal the explanation.
Short Explanation
Deep stacks can whisper the error so quietly it vanishes, or shout so loudly the numbers blow up. Those vanishing and exploding update stories are classic deep-training failure modes. They are conceptual diagnoses, not a call to author kernels.
Full Explanation
In very deep networks, gradients can shrink toward zero (vanishing) or grow without bound (exploding), disrupting stable weight updates. Both are recognized training failure modes for deep multimodal stacks. Associate depth names the modes without requiring cluster-fabric or kernel-level fixes.