Out-of-distribution (OOD) detection is critical for ensuring the safe deployment of machine learning models in the open world. Due to the simplicity and intuitiveness of distance- based methods, i.e., samples are detected as OOD if they are relatively far away from the centroids or prototypes of in-distribution (ID) classes, they have attracted widespread attention from researchers in the field of OOD detection. However, prior OOD detection methods directly take off-the- shelf loss functions, like widely used softmax cross-entropy (CE) loss, that suffices for classifying ID samples, but is not optimally designed for OOD detection. In this work, we propose CESED, an improved CE loss applied to the scalable Squared Euclidean Distance vector, which exploits hyper- spherical evenly-distributed class centroids for OOD detection. CESED can promote strong ID-OOD separability because it explicitly encourages maximization of inter-class distances and minimization of intra-class distances. Extensive experiments demonstrate that CESED achieves superior detection performance on a comprehensive suite of benchmark datasets. For the more challenging case where CIFAR-100 is used as ID, our method achieves a 31.98% reduction in average FPR95 and 6.20% reduction in ID test error compared to the baseline method using a softmax confidence score.

Time series has wide applications in the real world and is known to be difficult to forecast. Since its statistical properties change over time, its distribution also changes temporally, which will cause severe distribution shift problem to existing methods. However, it remains unexplored to model the time series in the distribution perspective. In this paper, we term this as Temporal Covariate Shift (TCS). This paper proposes Adaptive RNNs (AdaRNN) to tackle the TCS problem by building an adaptive model that generalizes well on the unseen test data. AdaRNN is sequentially composed of two novel algorithms. First, we propose Temporal Distribution Characterization to better characterize the distribution information in the TS. Second, we propose Temporal Distribution Matching to reduce the distribution mismatch in TS to learn the adaptive TS model. AdaRNN is a general framework with flexible distribution distances integrated. Experiments on human activity recognition, air quality prediction, and financial analysis show that AdaRNN outperforms the latest methods by a classification accuracy of 2.6% and significantly reduces the RMSE by 9.0%. We also show that the temporal distribution matching algorithm can be extended in Transformer structure to boost its performance.

Node classification is an important yet challenging task in various network applications, and many effective methods have been developed for a single network. While for cross-network scenarios, neither single network embedding nor traditional domain adaptation can directly solve the task. Existing approaches have been proposed to combine network embedding and domain adaptation for cross-network node classification. However, they only focus on domain-invariant features, ignoring the individual features of each network, and they only utilize 1-hop neighborhood information (local consistency), ignoring the global consistency information. To tackle the above problems, in this paper, we propose a novel model, Adversarial Separation Network(ASN), to learn effective node representations between source and target networks. We explicitly separate domain-private and domain-shared information. Two domain-private encoders are employed to extract the domain-specific features in each network and a shared encoder is employed to extract the domain-invariant shared features across networks. Moreover, in each encoder, we combine local and global consistency to capture network topology information more comprehensively. ASN integrates deep network embedding with adversarial domain adaptation to reduce the distribution discrepancy across domains. Extensive experiments on real-world datasets show that our proposed model achieves state-of-the-art performance in cross-network node classification tasks compared with existing algorithms.

Recently, deep convolutional neural networks (CNNs) are widely used in single image super-resolution (SISR) and have recorded impressive performance. However, most of the existing CNNs architectures can not fully utilize the correlation of feature maps in the middle layers, and abundant features of different levels are lost. Furthermore, convolution operation is limited by processing one local neighborhood at a time, which lacks global information. To address these issues, we propose the nested dense attention network (NDAN) for generating more refined and structured high-resolution images. Specifically, we propose nested dense structure (NDS) to better integrate features of different levels extracted from different layers. Besides that, in order to capture inter-channel dependencies more efficiently, we propose the adaptive channel attention module (ACAM) to adaptively rescale channel-wise features by automatically adjusting the weights of different receptive fields. Furthermore, to better explore the global-level context information, we design hybrid non-local module (HNLM) and hybrid non-local up-sampler (HNLU) to upscale the images by capturing spatial-wise long-distance dependencies and channel-wise long-distance correlation. Numerous experiments demonstrate the effectiveness of our model by achieving higher PSNR and SSIM scores and generating images with better structures against the state-of-the-art methods.