Semi-supervised learning (SSL) can reduce the need for large labelled datasets by incorporating unlabelled data into the training. This is particularly interesting for semantic segmentation, where labelling data is very costly and time-consuming. Current SSL approaches use an initially supervised trained model to generate predictions for unlabelled images, called pseudo-labels, which are subsequently used for training a new model from scratch. Since the predictions usually do not come from an error-free neural network, they are naturally full of errors. However, training with partially incorrect labels often reduce the final model performance. Thus, it is crucial to manage errors/noise of pseudo-labels wisely. In this work, we use three mechanisms to control pseudo-label noise and errors: (1) We construct a solid base framework by mixing images with cow-patterns on unlabelled images to reduce the negative impact of wrong pseudo-labels. Nevertheless, wrong pseudo-labels still have a negative impact on the performance. Therefore, (2) we propose a simple and effective loss weighting scheme for pseudo-labels defined by the feedback of the model trained on these pseudo-labels. This allows us to soft-weight the pseudo-label training examples based on their determined confidence score during training. (3) We also study the common practice to ignore pseudo-labels with low confidence and empirically analyse the influence and effect of pseudo-labels with different confidence ranges on SSL and the contribution of pseudo-label filtering to the achievable performance gains. We show that our method performs superior to state of-the-art alternatives on various datasets. Furthermore, we show that our findings also transfer to other tasks such as human pose estimation. Our code is available at https://github.com/ChristmasFan/SSL_Denoising_Segmentation.
翻译:半监督的学习(SSL) 可以通过将部分不正确的标签纳入培训来减少对大标记数据集的需求。 这对语义分解来说特别有趣, 因为在语义分解中, 标签数据非常昂贵且耗时。 当前 SSL 使用初步监督的训练有素模型来预测未贴标签图像, 称为假标签, 用于从零开始训练新模型。 由于预测通常不是来自无错误的神经网络, 它们自然会充满错误。 但是, 使用部分不正确的标签进行的培训往往会降低最终模型的性能。 因此, 明智地管理伪标签的错误/ 音义分解非常关键。 在此工作中, 我们使用三个机制来控制伪标签的噪音和错误:(1) 我们用未贴标签的图像来构建一个坚实的基础框架, 以减少错误的假标签的负面影响。 但是, 错误的伪标签仍然对SSS 性能产生负面的影响。 因此, 我们提出一个简单有效的伪标签增减计划。