In the present environment where privacy protection is increasingly emphasized, source-free unsupervised domain adaptation (SFUDA) has garnered more attention compared to standard unsupervised domain adaptation (UDA). It concentrates on transferring knowledge directly from well-trained source models to unlabeled target domains without requiring the involvement of source domain like UDA, greatly enhancing data protection capabilities. Many existing methods employ pseudo-labeling to guide this process, but due to domain shift, pseudo-labels often introduce significant noise. Although there are methods to filter out this noise and mitigate its impact, they may also result in the loss of crucial sample knowledge, leading to performance deterioration. In contrast, we propose a novel approach called Progressive Curriculum Learning with Teacher-Student Collaboration (PCTSC) method to mitigate the adverse influence of noisy labels in SFUDA. Inspired by curriculum learning, PCTSC assesses samples’ learning difficulty and trains models in an incremental manner from easy to hard, thereby enhancing the capability of model to against noise. Furthermore, PCTSC employs a two-stage learning approach: initially, a teacher model directs the student model, and later, the student model transitions to independent learning. We assess the effectiveness of PCTSC by conducting extensive experiments across three benchmark datasets, demonstrating its robustness against pseudo-label noise in SFUDA setting.

In meta-learning, the learner extracts knowledge from the observed tasks and quickly adapts to unseen future tasks. We provide a novel and rigorous-analyzed probably approximately correct Bayes (PAC-Bayes) meta-learning method with parameterized bounds, which learns a posterior distribution from given priors and the data samples. The proposed method is designed to improve generalization stabilities with tighter bound guarantees. We prove that the proposed PAC-Bayes bound of the meta-learner is tighter than previous work under a given condition in a rigorous theoretical way. An explicit theoretical analysis of the generalization errors is also given based on the proposed meta-learning method. Using the proposed bound in our work, we deduce an optimal objective function of the meta-learner that should be minimized during the meta-training process. We validate our theoretical hypothesis by conducting synthetic and real-world environments for meta-learning. Both rigorous proofs and experimental results reveal that our method yields state-of-the-art performances under a variety of meta-learning tasks in terms of accuracy and uncertainty robustness.

Human whole-body pose estimation is a challenging task since the model needs to learn more keypoints than the body-only case. To meet the needs of real-time performance while maintaining accuracy is also a hard issue in whole-body pose estimation due to the learning capability of lightweight networks. In order to solve the above problems to a large extent, we propose a light whole-body pose estimation method with an optimized training strategy. The model is designed based on bottom-up architecture as a base network followed by a refinement network. We propose a two-stage training process, which learns rough features in the first stage and then improves estimation precision in the second stage. An online data augmentation procedure is proposed in the second stage to improve refinement performance. We also introduce a separate learning refinement structure that fine-tunes for body, foot, and hand part independently. Experimental results show that our method improves over 8%–10% average precision compared with other lightweight state-of-the-art approaches in the whole-body pose estimation task, with nearly a quarter (25%) size of model parameters saved.

Logs are one of the most valuable data to describe the running state of services. Failure diagnosis through logs is crucial for service reliability and security. The current automatic log failure diagnosis methods cannot fully use the multiple fields of logs, which fail to capture the relation between them. In this article, we propose LogKG, a new framework for diagnosing failures based on knowledge graphs (KG) of logs. LogKG fully extracts entities and relations from logs to mine multi-field information and their relations through the KG. To fully use the information represented by KG, we propose a failure-oriented log representation (FOLR) method to extract the failure-related patterns. Utilizing the OPTICS clustering method, LogKG aggregates historical failure cases, labels typical failure cases, and trains a failure diagnosis model to identify the root cause. We evaluate the effectiveness of LogKG on a real-world log dataset and a public log dataset, respectively, showing that it outperforms existing methods. With the deployment in a top-tier global Internet Service Provider (ISP), we demonstrate the performance and practicability of LogKG.