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Semi-supervised training methods need reliable pseudo labels for unlabeled data. The current state-of-the-art methods based on pseudo labeling utilize only high-confidence predictions, whereas poor confidence predictions are discarded. This paper presents a novel approach to generate high-quality pseudo labels for unlabeled data. It utilizes predictions with high- and low-confidence levels to generate refined labels and then validates the accuracy of those predictions through bi-directional object tracking. The bi-directional object tracker leverages both past and future information to recover missing labels and increase the accuracy of the generated pseudo labels. This method can also substantially reduce the effort and time needed in label creation compared to the conventional manual labeling. The proposed method utilizes a buffer to accumulate detection labels (bounding boxes) predicted by the object detector. These labels are refined for accuracy though forward and backward tracking, ultimately constructing the final set of pseudo labels. The method is integrated in the YOLOv5 object detector and tested on the BDD100K dataset. Through the experiments, we demonstrate the effectiveness of the proposed scheme in automating the process of pseudo label generation with notably higher accuracy than the recent state-of-the-art pseudo label generation schemes. The results show that the proposed method outperforms previous methods in terms of mean average precision (mAP), label generation accuracy, and speed. Using the bi-directional recovery method, an increase in mAP@50 for the BDD100K dataset by 0.52% is achieved, and for the Waymo dataset, it provides an improvement of mAP@50 by 8.7% to 9.9% compared to 8.1% of the existing method when pre-training with 10% of the dataset. An improvement by 2.1% to 2.9% is achieved as compared to 1.7% of the existing method when pre-training with 20% of the dataset. Overall, the improved method leads to a significant enhancement in detection accuracy, achieving higher mAP scores across various datasets, thus demonstrating its robustness and effectiveness in diverse conditions. [ABSTRACT FROM AUTHOR]

Intelligent anomaly detection for wind turbines using deep-learning methods has been extensively researched and yielded significant results. However, supervised learning necessitates sufficient labeled data to establish the discriminant boundary, while unsupervised learning lacks prior knowledge and heavily relies on assumptions about the distribution of anomalies. A long short-term memory-based variational autoencoder Wasserstein generation adversarial network (LSTM-based VAE-WGAN) was established in this paper to address the challenge of small and noisy wind turbine datasets. The VAE was utilized as the generator, with LSTM units replacing hidden layer neurons to effectively extract spatiotemporal factors. The similarity between the model-fit distribution and true distribution was quantified using Wasserstein distance, enabling complex high-dimensional data distributions to be learned. To enhance the performance and robustness of the proposed model, a two-stage adversarial semi-supervised training approach was implemented. Subsequently, a monitoring indicator based on reconstruction error was defined, with the threshold set at a 99.7% confidence interval for the distribution curve fitted by kernel density estimation (KDE). Real cases from a wind farm in northeast China have confirmed the feasibility and advancement of the proposed model, while also discussing the effects of various applied parameters. [ABSTRACT FROM AUTHOR]

The detection of brain metastases (BM) in their early stages could have a positive impact on the outcome of cancer patients. The authors previously developed a framework for detecting small BM (with diameters of <15 mm) in T1-weighted contrast-enhanced 3D magnetic resonance images (T1c). This study aimed to advance the framework with a noisy-student-based self-training strategy to use a large corpus of unlabeled T1c data. Accordingly, a sensitivity-based noisy-student learning approach was formulated to provide high BM detection sensitivity with a reduced count of false positives. This paper (1) proposes student/teacher convolutional neural network architectures, (2) presents data and model noising mechanisms, and (3) introduces a novel pseudo-labeling strategy factoring in the sensitivity constraint. The evaluation was performed using 217 labeled and 1247 unlabeled exams via two-fold cross-validation. The framework utilizing only the labeled exams produced 9.23 false positives for 90% BM detection sensitivity, whereas the one using the introduced learning strategy led to ~9% reduction in false detections (i.e., 8.44). Significant reductions in false positives (>10%) were also observed in reduced labeled data scenarios (using 50% and 75% of labeled data). The results suggest that the introduced strategy could be utilized in existing medical detection applications with access to unlabeled datasets to elevate their performances. [ABSTRACT FROM AUTHOR]

Aiming at the audio event recognition problem of speech recognition, a decision fusion method based on the Transformer and Causal Dilated Convolutional Network (TCDCN) framework is proposed. This method can adjust the model sound events for a long time and capture the time correlation, and can effectively deal with the sparsity of audio data. At the same time, our dataset comes from audio clips cropped by YouTube. In order to reliably and stably identify audio topics, we extract different features and different loss function calculation methods to find the best model solution. The experimental results from different test models show that the TCDCN model proposed in this paper achieves better recognition results than the classification using neural networks and other fusion methods. [ABSTRACT FROM AUTHOR]

In this paper, we propose a policy gradient-based semi-supervised speech recognition acoustic model training. In practice, self-training and teacher/student learning are one of the widely used semi-supervised training methods due to their scalability and effectiveness. These methods are based on generating pseudo labels for unlabeled samples using a pre-trained model and selecting reliable samples using confidence measure. However, there are some considerations in this approach. The generated pseudo labels can be biased depending on which pre-trained model is used, and the training process can be complicated because the confidence measure is usually carried out in post-processing using external knowledge. Therefore, to address these issues, we propose a policy gradient method-based approach. Policy gradient is a reinforcement learning algorithm to find an optimal behavior strategy for an agent to obtain optimal rewards. The policy gradient-based approach provides a framework for exploring unlabeled data as well as exploiting labeled data, and it also provides a way to incorporate external knowledge in the same training cycle. The proposed approach was evaluated on an in-house non-native Korean recognition domain. The experimental results show that the method is effective in semi-supervised acoustic model training. [ABSTRACT FROM AUTHOR]