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Neural network pruning techniques can be effective in accelerating neural network models, making it possible to deploy them on edge devices. In this paper, we propose to prune neural networks using data variance. Unlike other existing methods, this method is somewhat robust and does not invalidate our criteria depending on the number of data batches and the number of training sessions. We also propose a pruning compensation technique. This technique fuses the pruned convolutional information into the remaining convolutional kernel close to it. This fusion operation can effectively help retain the pruned information. We evaluate the proposed method on a number of standard datasets and compare it with several current state-of-the-art methods. Our method always achieves better performance. For example, on Tiny ImageNet, our method can prune 54.2% FLOPs of ResNet50 while obtaining a 0.22% accuracy improvement. • A data-driven network pruning method with high robustness is proposed. • The proposed data-driven pruning method consumes less resources and is highly efficient. • A pruning compensation technique is proposed to reduce the negative impact of pruning. • The effectiveness of the proposed method is validated on several datasets. [ABSTRACT FROM AUTHOR]

• AMSFF-Net has ability to pay more attention to the informative features at different multi-stream resolution levels using pixel attention mechanism. • AMSFF-Net has ability to capture semantic information and sharp textural details from the extracted features and retain high-quality image from coarse-to-fine using mixed-convolution attention mechanism. • A sharp image can be recovered by the good kernel estimation. Mixed convolution attention mechanism highlights the task-relevant features and retain the dehazed images with sharp texture details and more vibrant colors. • Deep semantic loss highlights the boundary information and the spatial correlation in the deep features. In this paper, an end-to-end convolutional neural network is proposed to recover haze-free image named as Attention-Based Multi-Stream Feature Fusion Network (AMSFF-Net). The encoder-decoder network structure is used to construct the network. An encoder generates features at three resolution levels. The multi-stream features are extracted using residual dense blocks and fused by feature fusion blocks. AMSFF-Net has ability to pay more attention to informative features at different resolution levels using pixel attention mechanism. A sharp image can be recovered by the good kernel estimation. Further, AMSFF-Net has ability to capture semantic and sharp textural details from the extracted features and retain high-quality image from coarse-to-fine using mixed-convolution attention mechanism at decoder. The skip connections decrease the loss of image details from the larger receptive fields. Moreover, deep semantic loss function emphasizes more semantic information in deep features. Experimental findings prove that the proposed method outperforms in synthetic and real-world images. [ABSTRACT FROM AUTHOR]

Image denoising requires both spatial details and global contextualized information to recover a clean version from the deteriorative one. Previous deep convolution networks usually focus on modeling the local feature and stacked convolution blocks to expand the receptive field, which can catch the long-distance dependencies. However, contrary to the expectation, the extracted local feature incapacity recovers the global details by traditional convolution while the stacked blocks hinder the information flow. To tackle these issues, we introduce the M atrix Factorization D enoising Module (MD) to model the interrelationship between the global context aggregating process and the reconstructed process to attain the context details. Besides, we redesign a new basic block to ease the information flow and maintain the network performance. In addition, we conceive the F eature Fu sion Module (FFU) to fuse the information from the different sources. Inspired by the multi-stage progressive restoration architecture, we adopt two-stage convolution branches progressively reconstructing the denoised image. In this paper, we propose an original and efficient neural convolution network dubbed MFU. Experimental results on various image denoising datasets: SIDD, DND, and synthetic Gaussian noise datasets show that our MFU can produce comparable visual quality and accuracy results with state-of-the-art methods. • We propose MFU, an effective and efficient end-to-end multi-stage progressive convolution network with a new basic block for image denoising. • We represent the global structured information as the low-rank matrix and use the MD module to learn the contextually-enriched and globally structured feature. • Designing a module (FFU) fuses the multi-source information to promote the network reconstruct the image. [ABSTRACT FROM AUTHOR]

Image steganalysis based on convolutional neural networks(CNN) has attracted great attention. However, existing networks lack attention to regional features with complex texture, which makes the ability of discrimination learning miss in network. In this paper, we described a new CNN designed to focus on useful features and improve detection accuracy for spatial-domain steganalysis. The proposed model consists of three modules: noise extraction module, noise analysis module and classification module. A channel attention mechanism is used in the noise extraction module and analysis module, which is realized by embedding the SE(Squeeze-and-Excitation) module into the residual block. Then, we use convolutional pooling instead of average pooling to aggregate features. The experimental results show that detection accuracy of the proposed model is significantly better than those of the existing models such as SRNet, Zhu-Net and GBRAS-Net. Compared with these models, our model has better generalization ability, which is critical for practical application. • An end-to-end spatial steganalysis network CCNet is proposed. • Channel attention mechanism is introduced to focus on the region suitable for embedding secret information. • Convolution is used instead of average pooling to avoid the loss of steganographic information. • The proposed model is verified to have higher accuracy through comparative experiments. [ABSTRACT FROM AUTHOR]

Person re-identification(ReID) is an intelligent video surveillance technology that retrieves the same person from different cameras. This task is extremely challenging due to changes in person poses, different camera views, and occlusion. In recent years, person ReID based on deep learning technology has received widespread attention due to the rapid development and excellent performance of deep learning. In this paper, we first divide person ReID based on deep learning approaches into seven types, i.e., fused hand-crafted features deep model, representation learning model, metric learning model, part-based deep model, video-based model, GAN-based model, unsupervised model. Furthermore, we launched a brief overview of the seven types. Then, we introduce some examples of commonly used datasets, compare the performance of some algorithms on image and video datasets in recent years, and analyze the advantages and disadvantages of various methods. Finally, we summarize the possible future research directions of person ReID technology. • Dividing person ReID based on deep learning approaches into seven types. • Introducing some examples of commonly used datasets. • Comparing the performance of some algorithms on image and video datasets in recent years. • Analyzing the advantages and disadvantages of various methods. • Summarizing the possible future research directions of person ReID technology. [ABSTRACT FROM AUTHOR]

• An improved ASM architecture to extract handcrafted and deep features for AIFR. • ASM-CNN is a superior accessible solution for Rank-1 recognition. • It provides stronger performance as a classifier with ASM-CNN over CNN. Scientific efforts have expanded in age-invariant face recognition (AIFR). Matching faces of large age difference is, therefore, a problem, mostly because of a substantial disparity in the appearance of both young and old age. Owing to age, both the appearance and shape of the face are impaired, making recognition of the face the most challenging task. In recent years, AIFR has become a very common and demanding task. The set of feature extraction and classification algorithm is of prime importance in this field. As the numbers of features obtained from the datasets are large, there is a need to introduce a dimensionality reduction method to map high dimensionality feature space to low variance filter to form the final integrated face age model to be used in the classification process. In this paper, we introduced a novel concept of an improved Active Shape Model (ASM) in conjunction with a specially designed 7-layered Convolutional Neural Network (CNN) in order to accomplish a combination of feature extraction and classification in a single unit. The study approach involves conducting extensive experiments to evaluate the proposed system's performance using three standard datasets: FG-NET, LAG, and CACD. The results reveal that the proposed method outperforms state-of-the-art approaches and achieves excellent accuracy in face recognition across age. The maximum accuracies achieved by demonstrated ASM-CNN methodology for FG-NET, LAG, and CACD databases are 95.02%, 91.76 % and 99.4 % respectively. [ABSTRACT FROM AUTHOR]