论文标题
从非常高级的有条件嘈杂标签中学习的gan
GANs for learning from very high class conditional noisy labels
论文作者
论文摘要
我们使用生成对抗网络（GAN）来设计二进制分类的类有条件标签噪声（CCN）鲁棒方案。它首先从嘈杂标记的数据和0.1％或1％的清洁标签中生成一组正确标记的数据点，以使生成和真实的（清洁）标记的数据分布接近；生成的标记数据用于学习良好的分类器。通过使用Wasserstein Gan和简单的数据表示，可以避免模式崩溃问题在生成正确的特征标签对和偏斜特征标签维度比率（$ \ sim $ 784：1）的问题时（$ \ sim $ 784：1）。还提出了另一位具有信息理论风味的智慧。这两种方案的主要优点是在CCN速率很高的情况下，它们比现有方案的显着改善，而没有估计或交叉验证噪声速率。我们证明了清洁和嘈杂分布之间的KL差异增加了W.R.T.对称标签噪声模型中的噪声速率；可以扩展到高CCN率。这意味着由于甘恩斯的对抗性，我们的计划表现良好。此外，使用生成方法（学习清洁关节分布），而处理噪声使我们的方案的执行能力比GLC，LDMI和GCE等歧视方法更好。即使课程高度不平衡。我们使用Friedman F检验和Nemenyi PerthoC测试，我们表明，在高维二元类合成，MNIST和时尚MNIST数据集上，我们的方案的表现优于现有方法，并在噪声率上表现出一致的性能。
We use Generative Adversarial Networks (GANs) to design a class conditional label noise (CCN) robust scheme for binary classification. It first generates a set of correctly labelled data points from noisy labelled data and 0.1% or 1% clean labels such that the generated and true (clean) labelled data distributions are close; generated labelled data is used to learn a good classifier. The mode collapse problem while generating correct feature-label pairs and the problem of skewed feature-label dimension ratio ($\sim$ 784:1) are avoided by using Wasserstein GAN and a simple data representation change. Another WGAN with information-theoretic flavour on top of the new representation is also proposed. The major advantage of both schemes is their significant improvement over the existing ones in presence of very high CCN rates, without either estimating or cross-validating over the noise rates. We proved that KL divergence between clean and noisy distribution increases w.r.t. noise rates in symmetric label noise model; can be extended to high CCN rates. This implies that our schemes perform well due to the adversarial nature of GANs. Further, use of generative approach (learning clean joint distribution) while handling noise enables our schemes to perform better than discriminative approaches like GLC, LDMI and GCE; even when the classes are highly imbalanced. Using Friedman F test and Nemenyi posthoc test, we showed that on high dimensional binary class synthetic, MNIST and Fashion MNIST datasets, our schemes outperform the existing methods and demonstrate consistent performance across noise rates.
