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Missing values are inherent in multivariate time series because of multiple reasons, such as collection errors, which deteriorate the performance of follow-up analytic applications on the multivariate time series. Numerous missing value imputation methods have been proposed to mitigate the influence of missing values on multivariate time series analysis. Recently, inspired by the success of generative adversarial networks (GANs) in image generation, the GAN-2-Stage has been used to address the imputation problem with the generative model. Specifically, GAN-2-Stage employs GANs to impute the missing values. However, an extra phase is required to optimize the input random "noise" of the generator. In addition, the imputed values can be very different from real values because of the difficulty in training a GAN and the unstable generation process. Therefore, this paper proposes an end-to-end model to impute the missing values in a multivariate time series. Specifically, we introduce an encoder network into the standard GAN architecture that eliminates the input optimization phase in the GAN-2-Stage. Our generator utilizes real data during training to force the imputed values to be close to the real ones. Experiments on three real-world multivariate time series datasets demonstrate that the proposed model outperforms state-of-the-art methods in imputation tasks and downstream applications, including classification and regression. [ABSTRACT FROM AUTHOR]

Missing values are inherent in multivariate time series because of multiple reasons, such as collection errors, which deteriorate the performance of follow-up analytic applications on the multivariate time series. Numerous missing value imputation methods have been proposed to mitigate the influence of missing values on multivariate time series analysis. Recently, inspired by the success of generative adversarial networks (GANs) in image generation, the GAN-2-Stage has been used to address the imputation problem with the generative model. Specifically, GAN-2-Stage employs GANs to impute the missing values. However, an extra phase is required to optimize the input random "noise" of the generator. In addition, the imputed values can be very different from real values because of the difficulty in training a GAN and the unstable generation process. Therefore, this paper proposes an end-to-end model to impute the missing values in a multivariate time series. Specifically, we introduce an encoder network into the standard GAN architecture that eliminates the input optimization phase in the GAN-2-Stage. Our generator utilizes real data during training to force the imputed values to be close to the real ones. Experiments on three real-world multivariate time series datasets demonstrate that the proposed model outperforms state-of-the-art methods in imputation tasks and downstream applications, including classification and regression. [ABSTRACT FROM AUTHOR]

Given samples from a particular normal class, image novelty detection is aimed at determining whether a query sample is from the normal class. Images from this particular class are termed inliers or normal images, whereas images not belonging to the class are termed novelties. Most novelty detection approaches use deep neural networks. However, few of them are end-to-end, and state-of-the-art neural networks tend to be overconfident in their predictions. This may result in incorrect predictions and may negatively affect novelty detection. In this paper, we propose a novel model termed adversarially learned one-class novelty detection with confidence estimation for image novelty detection. The proposed model consists of a representation and a detection module, which are adversarially trained to collaboratively learn the inlier distribution. Moreover, the model uses confidence estimation so that the detection module can more effective. The proposed model is end-to-end and does not require additional calculations such as novelty scores after training. We conduct comprehensive experiments on four publicly available datasets that are commonly used for novelty detection, and the model is compared with state-of-the-art methods to demonstrate its performance. [ABSTRACT FROM AUTHOR]