The key differences between using Adam and SGD in RNN-GANs are:
1. Adaptive Learning Rates:
- Adam uses adaptive learning rates for each parameter, adjusting the step size based on the estimated first and second moments of the gradients.- SGD uses a fixed learning rate for all parameters, which can lead to slower convergence and less stable training.
2. Momentum:
- Adam includes momentum, which helps the model to converge faster and more robustly.- SGD does not include momentum, which can lead to slower convergence and less robust training.
3. Heavy-Tailed Noise:
- Adam is more robust to heavy-tailed noise in the stochastic gradients, which can occur in certain tasks like attention models.- SGD is less robust to heavy-tailed noise and can converge poorly in such cases.
4. Convergence:
- Adam tends to converge faster and more reliably than SGD, especially in tasks where the noise in stochastic gradients is heavy-tailed.- SGD can converge slower and less reliably than Adam in such cases.
5. Hyperparameter Tuning:
- Adam requires less hyperparameter tuning than SGD, as it adapts to the changing learning rate and momentum.- SGD requires more hyperparameter tuning, as the fixed learning rate and momentum need to be carefully set.
6. Performance:
- Adam tends to perform better than SGD in tasks where the noise in stochastic gradients is heavy-tailed, such as attention models.- SGD tends to perform better than Adam in tasks where the noise in stochastic gradients is well concentrated, such as ImageNet training.
7. Robustness:
- Adam is more robust to noisy data and can handle heavy-tailed noise better than SGD.- SGD is less robust to noisy data and can converge poorly in the presence of heavy-tailed noise.
8. Stability:
- Adam is more stable than SGD, as it adapts to the changing learning rate and momentum.- SGD can be less stable than Adam, especially in tasks where the noise in stochastic gradients is heavy-tailed.
9. Ensemble Methods:
- Adam can be used in ensemble methods to improve the performance and robustness of the model.- SGD can also be used in ensemble methods, but it may require more careful tuning of the hyperparameters.
10. Application-Specific Tuning:
- The optimal hyperparameter settings for Adam and SGD may vary depending on the specific application and dataset being used.- Careful experimentation and evaluation are necessary to determine the best hyperparameter configuration for a given RNN-GAN task.
In summary, Adam and SGD are both effective optimizers for RNN-GANs, but they have different strengths and weaknesses. Adam is more robust to heavy-tailed noise and tends to converge faster and more reliably, while SGD is more robust to well-concentrated noise and tends to perform better in tasks where the noise is well concentrated.
Citations:[1] https://www.researchgate.net/figure/Performance-of-the-RNN-with-ADAM-and-SGD-optimizers_tbl2_355371680
[2] https://stackoverflow.com/questions/45655156/why-does-neural-net-optimizer-change-from-sgd-to-adam-lead-to-massive-decrease-i
[3] https://datascience.stackexchange.com/questions/30344/why-not-always-use-the-adam-optimization-technique
[4] https://openreview.net/pdf?id=SJx37TEtDH
[5] https://www.researchgate.net/publication/321963357_Improving_Generalization_Performance_by_Switching_from_Adam_to_SGD