Semi-Supervised Learning by Augmented Distribution Alignment (1905.08171v2)

Abstract: In this work, we propose a simple yet effective semi-supervised learning approach called Augmented Distribution Alignment. We reveal that an essential sampling bias exists in semi-supervised learning due to the limited number of labeled samples, which often leads to a considerable empirical distribution mismatch between labeled data and unlabeled data. To this end, we propose to align the empirical distributions of labeled and unlabeled data to alleviate the bias. On one hand, we adopt an adversarial training strategy to minimize the distribution distance between labeled and unlabeled data as inspired by domain adaptation works. On the other hand, to deal with the small sample size issue of labeled data, we also propose a simple interpolation strategy to generate pseudo training samples. Those two strategies can be easily implemented into existing deep neural networks. We demonstrate the effectiveness of our proposed approach on the benchmark SVHN and CIFAR10 datasets. Our code is available at \url{https://github.com/qinenergy/adanet}.

• The paper introduces Augmented Distribution Alignment to address empirical distribution mismatches in semi-supervised settings.
• It employs adversarial training with a discriminator to align the latent spaces of labeled and unlabeled data.
• It further augments small labeled sets by generating pseudo-samples through cross-set interpolation, improving classification accuracy.

An Overview of "Semi-Supervised Learning by Augmented Distribution Alignment"

The paper entitled "Semi-Supervised Learning by Augmented Distribution Alignment" presents a novel approach to semi-supervised learning (SSL), essential for training robust models in scenarios with a limited number of labeled samples and an abundance of unlabeled data. The paper introduces an effective methodology addressing the empirical distribution mismatch between labeled and unlabeled samples, a factor often overlooked yet consequential in semi-supervised settings.

Key Contributions

The paper's primary contributions lie in proposing Augmented Distribution Alignment (ADA), which tackles the common problem of sampling bias in SSL by aligning the distributions of labeled and unlabeled datasets. The authors identify that empirical distributions observed in SSL conditions frequently differ due to the small sample size of labeled datasets compared to unlabeled ones, a factor that can potentially undermine model performance.

Two innovative strategies form the cornerstone of the ADA approach:

1. Adversarial Distribution Alignment: This involves adopting adversarial training mechanisms inspired by domain adaptation to minimize the distance between labeled and unlabeled data distributions. By integrating a discriminator in the neural network architecture, the ADA method aligns these distributions within the latent space, thereby mitigating sampling bias.
2. Cross-set Sample Augmentation: To address small sample sizes, the ADA introduces an interpolation strategy that generates pseudo-samples. This process involves mathematically blending labeled and unlabeled data points, which enriches the training set and reduces the sampling bias impact. These pseudo-samples are demonstrated to be closer to the underlying data distribution than their original counterparts.

Methodology

The paper details how both strategies can be seamlessly incorporated into existing deep neural networks. For instance, the Models are built upon adversarial networks utilizing a gradient reverse layer, an approach that requires minimal modifications to existing structures. This integration is exemplified through ADA-Net, a network substantiating the paper’s claims of improved performance through empirical evaluation.

Empirical Evaluation

The proposed methodology was extensively evaluated on benchmark datasets, namely SVHN and CIFAR10, demonstrating significant improvements over baseline methods. Specific improvements over traditional SSL approaches, such as the $\Pi$ Model, VAT, and Mean Teacher, highlight the effectiveness of ADA's alignment strategies. The novel approach also boasts superior classification performance, achieving state-of-the-art error rates, notably on SVHN.

Implications

By considering the empirical distribution mismatch in SSL, the paper illuminates a critical, previously understated challenge in machine learning practices. This insight has practical implications for enhancing SSL strategies across various domains, including computer vision and natural language processing, where labeled data can be scarce or expensive to obtain. Furthermore, the ADA methodology can potentially complement other SSL strategies, offering possibilities for hybrid approaches.

Speculation on Future Developments

Looking ahead, the ADA approach may serve as a foundation for further research into addressing empirical distribution mismatches in machine learning models. Integrating this strategy with other advanced techniques, like self-supervised learning, could prove particularly beneficial. Additionally, exploring its applications across diverse data types and settings, such as in real-time or dynamic data environments, could yield fruitful results.

In conclusion, "Semi-Supervised Learning by Augmented Distribution Alignment" offers a robust framework for enhancing performance in semi-supervised settings. By introducing a dual strategy that reduces the impact of empirical distribution mismatches, this work provides a substantive contribution to machine learning, particularly for tasks constrained by limited labeled data.