The crux of label-efficient semantic segmentation is to produce high-quality pseudo-labels to leverage a large amount of unlabeled or weakly labeled data. A common practice is to select the highly confident predictions as the pseudo-ground-truths for each pixel, but it leads to a problem that most pixels may be left unused due to their unreliability. However, we argue that every pixel matters to the model training, even those unreliable and ambiguous pixels. Intuitively, an unreliable prediction may get confused among the top classes, however, it should be confident about the pixel not belonging to the remaining classes. Hence, such a pixel can be convincingly treated as a negative key to those most unlikely categories. Therefore, we develop an effective pipeline to make sufficient use of unlabeled data. Concretely, we separate reliable and unreliable pixels via the entropy of predictions, push each unreliable pixel to a category-wise queue that consists of negative keys, and manage to train the model with all candidate pixels. Considering the training evolution, we adaptively adjust the threshold for the reliable-unreliable partition. Experimental results on various benchmarks and training settings demonstrate the superiority of our approach over the state-of-the-art alternatives.
Fault diagnosis in rolling bearings is an indispensable part of maintaining the normal operation of modern machinery, especially under the varying operating conditions. In this paper, an end-to-end adaptive anti-noise neural network framework (AAnNet) is proposed to solve the bearing fault diagnosis problem under heavy noise and varying load conditions, which takes the raw signal as input without requiring manual feature selection or denoising procedures. The proposed AAnNet employs the random sampling strategy and enhanced convolutional neural networks with the exponential linear unit as the activation function to increase the adaptability of the neural network. Moreover, the gated recurrent neural networks with attention mechanism improvement are further adopted to learn and classify the features processed by the convolutional neural networks part. Besides, we try to explain how the network works by visualizing the intrinsic features of the proposed framework. And we explore the effect of the attention mechanism in the proposed framework. Experiments show that the proposed framework achieves state-of-the-art results on two datasets under varying operating conditions.INDEX TERMS Bearing fault diagnosis, convolutional neural network, deep learning, load domain adaptation, noisy conditions, recurrent neural network.
Recently, deep neural networks have achieved great success in bearing fault diagnosis. Most existing methods are developed under the assumption that the bearing vibration signals are collected under the same machine operating conditions. However, bearing fault diagnosis under cross-domain conditions will suffer from domain shift problems if the neural network is only trained with the source domain data. Moreover, acquiring enough labeled data from the target domain will be expensive and time-consuming. To address the above problems, this paper proposes an end-to-end multi-adversarial cross-domain neural network for bearing fault diagnosis, which takes labeled source domain data and unlabeled target domain data to achieve the cross-domain bearing fault diagnosis under cross-load conditions and cross-machine conditions. The proposed method employs multi-adversarial training to automatically extract the domain-invariant features from source and target domains instead of manually designing features, which combines domain-adversarial learning and mini-max entropy adversarial learning to adversarially reduce the domain discrepancy between the source and target domains and alleviate the class misalignment problem. The results of the cross-load and the cross-machine experiments prove the effectiveness of the proposed method, and the proposed method provides a promising tool for cross-domain bearing fault diagnosis.
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