I am investigating the suitability of using deep learning for pricing and calibration for the full implied volatility surface. Such examples of their application are in papers here and here. During examples, the latter achieved the calibration task in mere milliseconds, which were orders of magnitude faster than numerical approximations and monte carlo methods.
However, I was wondering what some of the drawbacks of using Deep Learning for this task could be, and whether there were any papers or research that highlight this issue. The latter paper cited 'cumbersome computations' during training, but I was wondering if there were any drawbacks regarding, perhaps, the architecture of DL networks as well?