General Cyclical Training of Neural Networks (2202.08835v2)

Abstract: This paper describes the principle of "General Cyclical Training" in machine learning, where training starts and ends with "easy training" and the "hard training" happens during the middle epochs. We propose several manifestations for training neural networks, including algorithmic examples (via hyper-parameters and loss functions), data-based examples, and model-based examples. Specifically, we introduce several novel techniques: cyclical weight decay, cyclical batch size, cyclical focal loss, cyclical softmax temperature, cyclical data augmentation, cyclical gradient clipping, and cyclical semi-supervised learning. In addition, we demonstrate that cyclical weight decay, cyclical softmax temperature, and cyclical gradient clipping (as three examples of this principle) are beneficial in the test accuracy performance of a trained model. Furthermore, we discuss model-based examples (such as pretraining and knowledge distillation) from the perspective of general cyclical training and recommend some changes to the typical training methodology. In summary, this paper defines the general cyclical training concept and discusses several specific ways in which this concept can be applied to training neural networks. In the spirit of reproducibility, the code used in our experiments is available at \url{https://github.com/lnsmith54/CFL}.

• The paper presents a novel cyclical training method using an 'easy-hard-easy' regime that dynamically adjusts parameters throughout training.
• Key cyclical strategies such as weight decay, softmax temperature, and gradient clipping demonstrate improved test performance on datasets like CIFAR-10 and ImageNet.
• The approach reduces the need for extensive hyperparameter tuning, paving the way for automated systems and broader applications in neural network design.

An Analysis of General Cyclical Training for Neural Networks

The paper "General Cyclical Training of Neural Networks" by Leslie N. Smith presents a novel training methodology for neural networks termed as General Cyclical Training (GCT). This approach emphasizes a dynamic adjustment of training parameters, advocating for an "easy-hard-easy" training regime, where neural network training parameters shift across different epochs. The primary motivation stems from the observation that many training parameters held constant are unnecessary and counterproductive to optimal training outcomes.

Overview of General Cyclical Training

The backbone of General Cyclical Training is its flexibility to extend beyond static training paradigms, allowing hyperparameters, loss functions, data choices, and model structures to cycle through phases of varying difficulty. The structure includes initial and final epochs focused on easier training, akin to curriculum learning, while the middle epochs involve comprehensive, spectrum-spanning tasks conducive to robust generalization. GCT is theoretically grounded on existing premises that early and late epochs can benefit from easier configurations while leveraging the middle epochs for learning complex patterns.

Cyclical Methods

The paper introduces several cyclical strategies:

1. Cyclical Weight Decay (CWD): By varying the weight decay throughout the training process, networks achieved higher test performance across datasets like CIFAR-10 and ImageNet. Experimental results underline how CWD surpasses constant weight decay metrics, exemplifying the potential of adaptive regularization techniques.
2. Cyclical Softmax Temperature (CST): The use of dynamic temperature values within the softmax function illustrates improved model performance across multiple datasets. This approach hypothesizes that altering the temperature can align learning stages with different confidence thresholds, supporting more effective test accuracy.
3. Cyclical Gradient Clipping (CGC): This technique involves adjusting the clipping threshold based on the epoch, aiming to regulate gradient flow through the network dynamically. Initial findings suggest marginal improvements over static configurations.
4. Data-Based Cyclical Approaches: The paper extends cyclical concepts to data processing, advocating for cyclical data augmentation strategies and the structured integration of various data samples with varying complexities during training.
5. Cyclical Semi-Supervised Learning: Introduced as a future avenue, this method advocates a gradual introduction and reduction of unlabeled data augmentation, beginning with labeled data dominance and evolving towards an unsupervised configuration in mid-epochs, reverting for fine-tuning the final layers.

Implications and Future Directions

The implications of General Cyclical Training stand not only on immediate performance gains but also in scientific understanding and operational flexibility. The discourse suggests that cyclical trainings reduce the necessity for extensive hyperparameter tuning, offering broader applicable ranges. Consequently, these techniques may engender advancements in automated training systems and contribute to a more intuitive and theoretically-informed approach to neural network design from both empirical and pragmatic perspectives.

Future research may explore the efficacy of GCT in more diverse network architectures and tasks beyond classification, as well as refine the automated selection and transition strategies for cyclical parameters. Additionally, establishing robust metrics for model choice during different training phases remains crucial.

In conclusion, the paper consolidates cyclical training not merely as an assortment of empirical tricks but as an integrative principle, capable of enhancing the overall efficiency and intelligence of training regimens across disparate neural network applications.