In this paper, we focus on automatically colorizing single grayscale image without manual interventions. Most of existing methods tried to accurately restore unknown ground-truth colors and require paired training data for model optimization. However, the ideal restoration objective and strict training constraints limited their performance. Inspired by CycleGAN, we formulate the process of colorization as image-to-image translation and propose an effective color-CycleGAN solution. High-level semantic identity loss and low-level color loss are additionally suggested for model optimization. Our method allows using unpaired images for training and direct prediction in rgb color space, which makes training data collection much easier and more general. We train our model on randomly selected PASCAL VOC 2007 images. All ablation study on loss function and comparisons with state-of-the-art methods are performed on grayscale SUN data. The experiment results show that our improvements on training loss could achieve better content consistence and generate better reasonable colors with less artifacts. Moreover, due to the bidirectional nature of our model, our proposed method provides a by-product that gives an excellent alternative way on color image graying.

Automatic image colorization without manual interventions is an ill-conditioned and inherently ambiguous problem. Most of existing methods focus on formulating colorization as a regression problem and learn parametric mappings from grayscale to color through deep neural networks. Due to the multimodalities of color-grayscale space, in many applications, it is not required to recover exact ground-truth color. Pair-wise pixel-to-pixel learning-based algorithms lack rationality. Techniques such as color space conversion techniques are then proposed to avoid such direct pixel learning. However, the coloring results after color space conversion are blunt and unnatural. In this paper, we hold viewpoints that a reasonable solution is to generate some colorized result that looks natural. No matter what color a region is to be assigned, the colorized region should be semantically and spatially consistent. In this paper, we propose an effective semantic-aware automatic colorization model via unpaired cycle-consistent self-supervised network. Low-level monochrome loss, perceptual identity loss and high-level semantic-consistence loss, together with adversarial loss, are introduced to guide network self-training. We train and test our model on randomly selected subsets from PASCAL VOC 2012. The experimental results including human subjective studies demonstrate that, compared with state-of-the-art methods, our proposed model can achieve more convincing and superior results. Relevant source code is available at https://github.com/YuSuen/ACCycleGAN.

In this paper, we propose an iterative soft channel estimation and data detection algorithm based on a factor graph. Channel coefficients as well as data symbols are treated as variable nodes and are all estimated in a low-complexity element-wise manner. Applying asymmetric LDPC codes, this algorithm is able to deliver ambiguity-free outputs for MIMO systems with or without training symbols. Training symbols are inherently utilized as a type of a priori information. This algorithm thoroughly relaxes the troublesome constraints on training design in the sense that an arbitrary (even zero) number of training symbols can be placed at arbitrary positions within a data burst.

The automatic speaker verification system is sometimes vulnerable to various spoofing attacks. The 2-class Gaussian Mixture Model classifier for genuine and spoofed speech is usually used as the baseline for spoofing detection. However, the GMM classifier does not separately consider the scores of feature frames on each Gaussian component. In addition, the GMM accumulates the scores on all frames independently, and does not consider their correlations. We propose the two-path GMM-ResNet and GMM-SENet models for spoofing detection, whose input is the Gaussian probability features based on two GMMs trained on genuine and spoofed speech respectively. The models consider not only the score distribution on GMM components, but also the relationship between adjacent frames. A two-step training scheme is applied to improve the system robustness. Experiments on the ASVspoof 2019 show that the LFCC+GMM-ResNet system can relatively reduce min-tDCF and EER by 76.1% and 76.3% on logical access scenario compared with the GMM, and the LFCC+GMM-SENet system by 94.4% and 95.4% on physical access scenario. After score fusion, the systems give the second-best results on both scenarios.

In the modern era of big data, large-scale graph computing has become challenging because of the dramatic rise in graph data size. Graph edge partitioning (GEP) is a crucial preprocessing step to distributed graph platforms, yet it is challenging to partition the large-scale graphs. GEP has shown better partition quality than the graph vertex partitioning for the graph’s skewed degree distribution. Existing GEP approaches are classified into two as stream and offline. The former category assigns edges to the partitions based on the previously received edge information. It has less partitioning quality and is affected by stream order compared to the latter while supporting big graph partitioning. The latter uses complete knowledge of a graph during partitioning and hence has a better partitioning quality than the former; however, it does not support large-scale graphs. In this study, we propose a novel OffStream partitioning approach (OSPA) and hybrid graph edge partitioner OffStreamNH. OSPA leverages both the offline and stream graph partitioning approaches through stateful partitioning by introducing a state layer. This stateful partition state is recorded while offline is partitioning its input graph. It contains partial knowledge of previously partitioned data and is used by the stream partitioner. The OffStreamNH uses Neighborhood Expansion (NE) and Higher Degree Replicated First (HDRF) algorithms for the offline and online; respectively, with minor modifications of both algorithms. Experimental results show that OffStreamNH outperforms the state of the art stream partitioners in terms of replication factor, load balance and tolerates the effect of stream orders.