论文标题
置信度得分使实例依赖性标签 - 噪声学习成为可能
Confidence Scores Make Instance-dependent Label-noise Learning Possible
论文作者
论文摘要
在使用嘈杂标签的学习中，对于每种情况，其标签都可以在被称为噪声模型的过渡分布后随机步行到其他类。经过良好的噪声模型都是独立于实例的，即过渡仅取决于原始标签，但不取决于实例本身，因此它们在野外不太实用。幸运的是，已经研究了基于实例依赖性噪声的方法，但是大多数必须依靠对噪声模型的强有力的假设。为了减轻此问题，我们引入了置信度评分的实例依赖性噪声（CSIDN），其中每个实例标签对配备了置信度得分。我们在置信分数的帮助下发现，每个实例的过渡分布都可以估算。与实例无关的噪声的强大正向校正类似，我们提出了一种新颖的实例级向前校正CSIDN。我们通过在合成标签噪声和现实世界未知噪声下进行多个实验来证明我们方法的实用性和有效性。
In learning with noisy labels, for every instance, its label can randomly walk to other classes following a transition distribution which is named a noise model. Well-studied noise models are all instance-independent, namely, the transition depends only on the original label but not the instance itself, and thus they are less practical in the wild. Fortunately, methods based on instance-dependent noise have been studied, but most of them have to rely on strong assumptions on the noise models. To alleviate this issue, we introduce confidence-scored instance-dependent noise (CSIDN), where each instance-label pair is equipped with a confidence score. We find with the help of confidence scores, the transition distribution of each instance can be approximately estimated. Similarly to the powerful forward correction for instance-independent noise, we propose a novel instance-level forward correction for CSIDN. We demonstrate the utility and effectiveness of our method through multiple experiments under synthetic label noise and real-world unknown noise.
