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- Read more about Multi-step Online Unsupervised Domain Adaptation
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In this paper, we address the Online Unsupervised Domain Adaptation (OUDA) problem, where the target data are unlabelled and arriving sequentially. The traditional methods on the OUDA problem mainly focus on transforming each arriving target data to the source domain, and they do not sufficiently consider the temporal coherency and accumulative statistics among the arriving target data. We propose a multi-step framework for the OUDA problem, which institutes a novel method to compute the mean-target subspace inspired by the geometrical interpretation on the Euclidean space.
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- Read more about HIGH-ACCURACY CLASSIFICATION OF ATTENTION DEFICIT HYPERACTIVITY DISORDER WITH L2,1-NORM LINEAR DISCRIMINANT ANALYSIS
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- Read more about Multispectral Fusion of RGB and NIR Images Using Weighted Least Squares and Alternating Guidance
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In low light condition, color (RGB) images captured by camera contain much noise and loss of details and color. However, near infrared (NIR) images are robust to noise and have clear textures without color. In this paper, we propose multi-spectral fusion of RGB and NIR images using weighted least squares (WLS) and alternating guidance. Low light RGB images provide coarse image structure and color, while NIR images offer clear textures in a short distance. Since they are complementary, we adopt alternating guidance for fusion of RGB and NIR images based on WLS.
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- Read more about Joint Sparse Recovery using Deep Unfolding With Application to Massive Random Access
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- Read more about Indoor Altitude Estimation of Unmanned Aerial Vehicles Using a Bank of Kalman Filters
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Altitude estimation is important for successful control and navigation of unmanned aerial vehicles (UAVs). UAVs do not have indoor access to GPS signals and can only use on-board sensors for reliable estimation of altitude. Unfortunately, most existing navigation schemes are not robust to the presence of abnormal obstructions above and below the UAV.
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- Read more about Learning Product Graphs from Multidomain Signals
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In this paper, we focus on learning the underlying product graph structure from multidomain training data. We assume that the product graph is formed from a Cartesian graph product of two smaller factor graphs. We then pose the product graph learning problem as the factor graph Laplacian matrix estimation problem. To estimate the factor graph Laplacian matrices, we assume that the data is smooth with respect to the underlying product graph.
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- Read more about Location-Relative Attention Mechanisms For Robust Long-Form Speech Synthesis
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Despite the ability to produce human-level speech for in-domain text, attention-based end-to-end text-to-speech (TTS) systems suffer from text alignment failures that increase in frequency for out-of-domain text. We show that these failures can be addressed using simple location-relative attention mechanisms that do away with content-based query/key comparisons. We compare two families of attention mechanisms: location-relative GMM-based mechanisms and additive energy-based mechanisms.
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- Read more about Generative pre-training for speech with autoregressive predictive coding
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- Read more about MANet: Multi-Scale Aggregated Network For Light Field Depth Estimation
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