There Are Many Consistent Explanations of Unlabeled Data: Why You Should Average (1806.05594v3)

Abstract: Presently the most successful approaches to semi-supervised learning are based on consistency regularization, whereby a model is trained to be robust to small perturbations of its inputs and parameters. To understand consistency regularization, we conceptually explore how loss geometry interacts with training procedures. The consistency loss dramatically improves generalization performance over supervised-only training; however, we show that SGD struggles to converge on the consistency loss and continues to make large steps that lead to changes in predictions on the test data. Motivated by these observations, we propose to train consistency-based methods with Stochastic Weight Averaging (SWA), a recent approach which averages weights along the trajectory of SGD with a modified learning rate schedule. We also propose fast-SWA, which further accelerates convergence by averaging multiple points within each cycle of a cyclical learning rate schedule. With weight averaging, we achieve the best known semi-supervised results on CIFAR-10 and CIFAR-100, over many different quantities of labeled training data. For example, we achieve 5.0% error on CIFAR-10 with only 4000 labels, compared to the previous best result in the literature of 6.3%.

Overview of "There are Many Consistent Explanations of Unlabeled Data: Why You Should Average"

The paper "There are Many Consistent Explanations of Unlabeled Data: Why You Should Average" investigates semi-supervised learning techniques, particularly consistency regularization approaches, and proposes the application of Stochastic Weight Averaging (SWA) and its modified variant fast-SWA to enhance model performance. The authors challenge the convergence behavior of Stochastic Gradient Descent (SGD) in semi-supervised settings and leverage SWA to stabilize and enhance the solutions obtained.

Key Contributions

1. Consistency Regularization Analysis: The work explores consistency regularization methods in semi-supervised learning, where models are trained to produce stable predictions under small perturbations. Notably, techniques such as the model and Mean Teacher, which are based on consistency loss, are examined for their capacity to increase generalization by stabilizing models against disturbances in inputs or weights.
2. Training Dynamics and Parameter Space Exploration: The authors explore how SGD behaves on consistency-based methods, revealing that SGD struggles to settle on a single point in parameter space and instead explores a wider set of potential solutions. The paper highlights that even with a consistent loss approach, SGD trajectories do not converge tightly, resulting in continued fluctuations in model predictions and performance.
3. Stochastic Weight Averaging (SWA): The proposed solution, SWA, involves averaging model weights over the course of SGD training to improve generalization. SWA operates over cyclic learning rates, capturing weights at various stages of the learning curve. The empirical paper demonstrates SWA's effectiveness in achieving the best semi-supervised error rates on CIFAR-10 and CIFAR-100 benchmarks, significantly outperforming previous results.
4. Fast-SWA for Accelerated Convergence: To accelerate the convergence of SWA, the authors propose fast-SWA, which collects weights more frequently within each cycle of learning rate schedules. This approach hastens training while maintaining or improving error performance, as evidenced by empirical results.
5. Improved State-of-the-Art: With these techniques, the paper achieves state-of-the-art results in semi-supervised learning settings, demonstrating substantial improvements on the CIFAR-10 and CIFAR-100 datasets. For instance, a 5.0% error rate is achieved on CIFAR-10 with only 4,000 labeled samples, surpassing previous best-known results.

Implications

The research extends the understanding of semi-supervised learning through the lens of training dynamics and model stability. By showing that SWA, and particularly fast-SWA, can produce more stable solutions in non-convergent scenarios, the paper emphasizes the potential to enhance generalization performance significantly. The broader implications suggest that weight averaging could be utilized in various learning paradigms, including reinforcement learning and adversarial settings, where model stability in exploration phases is crucial.

Future Directions

Future explorations could focus on adopting SWA in other domains such as natural language processing or reinforcement learning, where similar insights into training trajectories and model stability could yield performance gains. Additionally, further theoretical exploration into the convergence properties and geometry of loss landscapes in more complex architectures and novel machine learning tasks could provide richer insights and refined practices.

In summary, "There are Many Consistent Explanations of Unlabeled Data: Why You Should Average" presents a robust analysis of semi-supervised learning strategies, contributing significantly to the methodological toolkit available for improving model performance in settings with limited labeled data.