Nowadays, deep learning has achieved remarkable results in many computer vision related tasks, among which the support of big data is essential. In this paper, we propose a full stage data augmentation framework to improve the accuracy of deep convolutional neural networks, which can also play the role of implicit model ensemble without introducing additional model training costs. Simultaneous data augmentation during training and testing stages can ensure network optimization and enhance its generalization ability. Augmentation in two stages needs to be consistent to ensure the accurate transfer of specific domain information. Furthermore, this framework is universal for any network architecture and data augmentation strategy and therefore can be applied to a variety of deep learning based tasks. Finally, experimental results about image classification on the coarse-grained dataset CIFAR-10 (93.41%) and fine-grained dataset CIFAR-100 (70.22%) demonstrate the effectiveness of the framework by comparing with state-of-the-art results.

Nowadays, despite the popularity of deep convolutional neural networks (CNNs), the efficient training of network models remains challenging due to several problems. In this paper, we present a layer-wise learning based stochastic gradient descent method (LLb-SGD) for gradient-based optimization of objective functions in deep learning, which is simple and computationally efficient. By simulating the cross-media propagation mechanism of light in the natural environment, we set an adaptive learning rate for each layer of neural networks. In order to find the proper local optimum quickly, the dynamic learning sequence spanning different layers adaptively adjust the descending speed of objective function in multi-scale and multi-dimensional environment. To the best of our knowledge, this is the first attempt to introduce an adaptive layer-wise learning schedule with a certain degree of convergence guarantee. Due to its generality and robustness, the method is insensitive to hyper-parameters and therefore can be applied to various network architectures and datasets. Finally, we show promising results compared to other optimization methods on two image classification benchmarks using five standard networks.

With the continuous advancement of wireless communication and the emergence of new communication scenarios, channel estimation, as a core component of wireless system design, has become increasingly significant. This paper reviews important advancements in channel estimation within wireless communication systems, including applications in single-input single-output (SISO), multi-input multi-output (MIMO), orthogonal time frequency space (OTFS), orthogonal frequency division multiplexing (OFDM), and the latest reconfigurable intelligent surface (RIS) systems. We first revisit traditional channel estimation methods, such as least squares (LS), minimum mean square error (MMSE), and compressed sensing (CS), and detail their fundamental principles and scopes of application. Subsequently, we discuss how deep learning techniques offer new perspectives and solutions for channel estimation through models like convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), long short-term memory (LSTM), and graph neural network (GNN), particularly in terms of their potential to handle complicated and dynamic environments. Additionally, we analyze the advantages and disadvantages of these methods in emerging scenarios, including RIS-assisted communications, vehicular networks, indoor positioning, sensing mobile networks, and satellite communications. We also address current methods for evaluating channel estimation performance and highlight the importance of standardization and open data in advancing the field. Finally, we summarize potential future directions for channel estimation and consider its prospects in sixth-generation (6 G) wireless communication systems, aiming to provide a comprehensive technical reference on channel estimation and promote the design of efficient and intelligent wireless communication systems.