论文标题
一种简单的概率方法，用于在输入依赖性标签噪声下进行深层分类
A Simple Probabilistic Method for Deep Classification under Input-Dependent Label Noise
论文作者
论文摘要
具有嘈杂标签的数据集是分类方法的实际应用中的常见情况。我们提出了一种简单的概率方法，用于训练在输入依赖性（异源性）标签噪声下训练深层分类器。我们假设嘈杂标签的潜在异质生成过程。为了使基于梯度的训练可行，我们使用温度参数化的软max作为对假定生成过程的平滑近似。我们说明，软磁性温度控制近似值的偏置方差权衡权衡。通过调整软磁体温度，我们在两个图像分类基准上提高了具有控制标签噪声以及Imagenet-21K的精度，模拟样和校准，并且自然存在标签噪声。对于图像进行分割，我们的方法将Pascal VOC和CityScapes数据集上的平均值增加了1％以上。
Datasets with noisy labels are a common occurrence in practical applications of classification methods. We propose a simple probabilistic method for training deep classifiers under input-dependent (heteroscedastic) label noise. We assume an underlying heteroscedastic generative process for noisy labels. To make gradient based training feasible we use a temperature parameterized softmax as a smooth approximation to the assumed generative process. We illustrate that the softmax temperature controls a bias-variance trade-off for the approximation. By tuning the softmax temperature, we improve accuracy, log-likelihood and calibration on both image classification benchmarks with controlled label noise as well as Imagenet-21k which has naturally occurring label noise. For image segmentation, our method increases the mean IoU on the PASCAL VOC and Cityscapes datasets by more than 1% over the state-of-the-art model.
