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Summary: This paper proposes a novel unsupervised domain adaptation method that improves the transfer of domain knowledge by mutual distillation between two models with complementary properties (i.e. CNNs and ViTs). It also introduces a patch-mixup method to encourage cross-domain information interaction and a cross-domain semantic alignment loss to align the semantic information of different domains. Extensive experiments demonstrate that this method achieves state-of-the-art results on various standard UDA benchmarks.
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Summary: In this paper, a Double Incremental Learning algorithm via Adaptive Ensemble (AE-DIL) is proposed for non-stationary time series prediction. AE-DIL consists of two modules: change detection and prediction model updating based on double incremental learning and adaptive ensemble learning. The proposed algorithm is empirically validated on benchmark time series datasets and outperforms several baselines and state-of-the-art models.
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Summary: This paper addresses the issue of concept-drift in machine learning in high dimensional problems. It introduces a method that assesses concept drift as an independent characteristic of dataset evolution, regardless of the specific target variable. This method is particularly useful in business environments where the same dataset is used for multiple classifications. Unlike previous methods, it does not require re-testing of each new model, saving computational resources.
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(2022)
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Summary: This paper proposes CAE-AD, a novel contrastive autoencoder for anomaly detection in multivariate time series (MTS) data. By introducing multi-grained contrasting methods, this method can capture the dependencies and dynamics of MTS, extract the normal data pattern, and improve the performance of anomaly detection.
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(2022)
Article
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Summary: This paper proposes a Softmax-Based Prototype construction and Adaptation (SBPA) method, which achieves domain-level and class-level alignment through aligning prototypes and adversarial training, and explicitly models the difference between source and target domain features using a residual block.
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(2022)
Article
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Summary: Learning temporal patterns in time series, especially for anomaly detection, remains challenging. The TCN-AE, an unsupervised temporal convolutional network autoencoder based on dilated convolutions, significantly outperforms other state-of-the-art anomaly detection algorithms on a real-world benchmark. Each new enhancement contributes to improving the overall performance of the algorithm.
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Article
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Summary: Splicing Learning proposes a method for completing few-shot learning tasks without the need for large homogeneous-annotated datasets, achieving good results in experiments by increasing sample size without false information in the spliced images.
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Summary: To address security concerns in communication networks, a semi-supervised anomaly detection framework called SemiADC is proposed, which improves the accuracy of anomaly detection through self-learning processes.
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(2021)
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Mine Kerpicci et al.
Summary: The proposed algorithm is an unsupervised anomaly detection algorithm that can handle sequential data from complex distributions online with strong performance guarantees. By constructing a partitioning tree and training online kernel density estimators, the algorithm sequentially produces a final density estimation for anomaly detection by comparing it with a data-adaptive threshold. The computational complexity is linear in both tree depth and data length, and significant improvements in accuracy compared to state-of-the-art techniques have been observed in experiments.
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(2015)
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