While randomization techniques offer benefits in improving deep learning performance, there are potential drawbacks and limitations to consider when relying heavily on them. One limitation is the increased computational cost associated with implementing complex randomization strategies, which can slow down the training process and require more resources. This issue can be mitigated by optimizing the implementation of randomization techniques, using parallel processing, or leveraging hardware accelerators like GPUs.
Another drawback is the risk of introducing too much randomness, which can lead to model instability or convergence issues. To mitigate this, it is essential to carefully tune the hyperparameters of the randomization techniques and monitor the model's performance during training. Regular validation checks and early stopping criteria can help prevent overfitting or underfitting caused by excessive randomness.
Additionally, relying solely on randomization techniques without a deep understanding of their interactions and effects on the model can result in suboptimal performance. To address this limitation, thorough experimentation, ablation studies, and analysis of the impact of each randomization technique on the model's performance are crucial. This can help in identifying the most effective combinations and fine-tuning the randomness settings for optimal results.