Smooth Neighbors on Teacher Graphs for Semi-supervised Learning (1711.00258v2)

Abstract: The recently proposed self-ensembling methods have achieved promising results in deep semi-supervised learning, which penalize inconsistent predictions of unlabeled data under different perturbations. However, they only consider adding perturbations to each single data point, while ignoring the connections between data samples. In this paper, we propose a novel method, called Smooth Neighbors on Teacher Graphs (SNTG). In SNTG, a graph is constructed based on the predictions of the teacher model, i.e., the implicit self-ensemble of models. Then the graph serves as a similarity measure with respect to which the representations of "similar" neighboring points are learned to be smooth on the low-dimensional manifold. We achieve state-of-the-art results on semi-supervised learning benchmarks. The error rates are 9.89%, 3.99% for CIFAR-10 with 4000 labels, SVHN with 500 labels, respectively. In particular, the improvements are significant when the labels are fewer. For the non-augmented MNIST with only 20 labels, the error rate is reduced from previous 4.81% to 1.36%. Our method also shows robustness to noisy labels.

• The paper introduces SNTG, which constructs teacher graphs to enforce smoothness between neighbors on the data manifold in semi-supervised learning.
• It achieves state-of-the-art results with error rates of 9.89% on CIFAR-10, 3.99% on SVHN, and 1.36% on MNIST, demonstrating significant performance gains.
• The method integrates seamlessly into existing frameworks without extra parameters, offering robustness against noisy labels in real-world applications.

Smooth Neighbors on Teacher Graphs for Semi-supervised Learning

The presented paper explores the nuanced domain of semi-supervised learning (SSL) by proposing a novel method named Smooth Neighbors on Teacher Graphs (SNTG). This work builds upon the existing framework of self-ensembling methods, which traditionally penalize inconsistencies in predictions of unlabeled data when subjected to different perturbations. The primary contribution of SNTG lies in its ability to transcend the limitations of prior methods by considering the interconnections between data samples, thus adhering to the manifold assumption critical in SSL.

Core Methodology

The SNTG method introduces a teacher graph constructed based on the predictions of the teacher model — effectively an implicit self-ensemble of models. This graph acts as a similarity measure, ensuring that the representations of neighboring points on the perceived low-dimensional manifold are learned to be smooth. The central idea is to impose a stronger regularization by fostering smoothness not merely at individual data points but across neighboring points, thereby unveiling and utilizing the nuanced structure within the data.

Numerical Results

In terms of performance metrics on standard SSL benchmarks, SNTG demonstrates exemplary improvements, achieving state-of-the-art error rates on some datasets. For instance, error rates of 9.89% on CIFAR-10 with 4000 labels, 3.99% on SVHN with 500 labels, and a significant reduction in error rate to 1.36% on MNIST with merely 20 labels, represent substantial gains. These metrics underscore the method’s efficacy particularly when labeled data is sparse, reinforcing the utility of leveraging unlabelled data structures.

Implications and Future Directions

Practically, SNTG can be seamlessly integrated into existing deep SSL frameworks without introducing extra network parameters or substantial computational overhead. The approach's robustness against noisy labels further enhances its applicability in real-world scenarios where label noise is prevalent. Theoretically, SNTG embodies an intuitive implementation of the manifold assumption by rigorously enforcing smoothness, which furthers the understanding of semi-supervised learning dynamics.

Future developments in the field of SSL could focus on exploring more sophisticated graph construction techniques or alternative manifold regularization strategies that potentially further enhance generalization performance. Additionally, extending SNTG to large-scale datasets and deeper network architectures could yield further insights and performance improvements, particularly in cases where extractable structures within the data become complex.

In summary, the SNTG method marks a significant advancement in SSL by innovatively utilizing manifold structures and smoothness, demonstrating noteworthy improvements in error metrics, and providing a robust, extensible model that can adapt to real-world label challenges. Its integration into generative models or scenarios requiring high label precision will be imperative for next-generation SSL advancements.