Optimizer Innovations and Pruning Insights
Recent developments in the field of deep learning optimization and model pruning have introduced several innovative approaches that significantly advance the state-to-state. Optimizer advancements are focusing on enhancing the efficiency and robustness of training processes, particularly in the face of noisy data and dynamic learning environments. Techniques such as Exponential Moving Average (EMA) of weights are being recognized not just for their role in improving generalization but also for their ability to provide robustness against noisy labels and improved transfer learning capabilities. Additionally, momentum-based optimizers are being re-evaluated through frequency domain analysis, leading to the proposal of new methods like Frequency Stochastic Gradient Descent with Momentum (FSGDM) that dynamically adjust momentum characteristics to enhance performance.
In the realm of model pruning, there is a growing recognition of the limitations of traditional oracle pruning methods, particularly in the context of modern deep learning models. Empirical evidence suggests that the performance of pruned models before and after retraining shows little correlation, questioning the validity of current pruning criteria. This has led to a call for rethinking the retraining stage in pruning algorithms to develop more effective criteria.
Noteworthy papers include:
- Exponential Moving Average of Weights in Deep Learning: Dynamics and Benefits: Demonstrates EMA's effectiveness in improving model robustness and transfer learning.
- On the Performance Analysis of Momentum Method: A Frequency Domain Perspective: Introduces FSGDM, a novel optimizer that dynamically adjusts momentum based on frequency domain insights.
- Is Oracle Pruning the True Oracle?: Challenges the validity of oracle pruning in modern deep learning models, advocating for a reevaluation of pruning criteria.
