We introduce a new family of deep neural networks, where instead of the conventional representation of network layers as N-dimensional weight tensors, we use a continuous layer representation along the filter and channel dimensions. We call such networks Integral Neural Networks (INNs). In particular, the weights of INNs are represented as continuous functions defined on N-dimensional hypercubes, and the discrete transformations of inputs to the layers are replaced by continuous integration operations, accordingly. During the inference stage, our continuous layers can be converted into the traditional tensor representation via numerical integral quadratures. Such kind of representation allows the discretization of a network to an arbitrary size with various discretization intervals for the integral kernels. This approach can be applied to prune the model directly on an edge device while suffering only a small performance loss at high rates of structural pruning without any fine-tuning. To evaluate the practical benefits of our proposed approach, we have conducted experiments using various neural network architectures on multiple tasks. Our reported results show that the proposed INNs achieve the same performance with their conventional discrete counterparts, while being able to preserve approximately the same performance (2% accuracy loss for ResNet18 on Imagenet) at a high rate (up to 30%) of structural pruning without fine-tuning, compared to 65% accuracy loss of the conventional pruning methods under the same conditions. Code is available at gitee.

This paper studies the single image super-resolution problem using adder neural networks (AdderNets). Com-pared with convolutional neural networks, AdderNets utilize additions to calculate the output features thus avoid massive energy consumptions of conventional multiplications. However, it is very hard to directly inherit the existing success of AdderNets on large-scale image classification to the image super-resolution task due to the different calculation paradigm. Specifically, the adder operation cannot easily learn the identity mapping, which is essential for image processing tasks. In addition, the functionality of high-pass filters cannot be ensured by AdderNets. To this end, we thoroughly analyze the relationship between an adder operation and the identity mapping and insert shortcuts to enhance the performance of SR models using adder networks. Then, we develop a learnable power activation for adjusting the feature distribution and refining details. Experiments conducted on several benchmark models and datasets demonstrate that, our image super-resolution models using AdderNets can achieve comparable performance and visual quality to that of their CNN baselines with an about 2.5× reduction on the energy consumption. The codes are available at: https://github.com/huawei-noah/AdderNet.

Deep learning based methods, especially convolutional neural networks (CNNs) have been successfully applied in the field of single image super-resolution (SISR). To obtain better fidelity and visual quality, most of existing networks are of heavy design with massive computation. However, the computation resources of modern mobile devices are limited, which cannot easily support the expensive cost. To this end, this paper explores a novel frequency-aware dynamic network for dividing the input into multiple parts according to its coefficients in the discrete cosine transform (DCT) domain. In practice, the high-frequency part will be processed using expensive operations and the lower-frequency part is assigned with cheap operations to relieve the computation burden. Since pixels or image patches belong to low-frequency areas contain relatively few textural details, this dynamic network will not affect the quality of resulting super-resolution images. In addition, we embed predictors into the proposed dynamic network to end-to-end fine-tune the handcrafted frequency-aware masks. Extensive experiments conducted on benchmark SISR models and datasets show that the frequency-aware dynamic network can be employed for various SISR neural architectures to obtain the better tradeoff between visual quality and computational complexity. For instance, we can reduce the FLOPs of SR models by approximate 50% while preserving state-of-the-art SISR performance.

Although remarkable progress has been made on single image super-resolution due to the revival of deep convolutional neural networks, deep learning methods are confronted with the challenges of computation and memory consumption in practice, especially for mobile devices. Focusing on this issue, we propose an efficient residual dense block search algorithm with multiple objectives to hunt for fast, lightweight and accurate networks for image super-resolution. Firstly, to accelerate super-resolution network, we exploit the variation of feature scale adequately with the proposed efficient residual dense blocks. In the proposed evolutionary algorithm, the locations of pooling and upsampling operator are searched automatically. Secondly, network architecture is evolved with the guidance of block credits to acquire accurate super-resolution network. The block credit reflects the effect of current block and is earned during model evaluation process. It guides the evolution by weighing the sampling probability of mutation to favor admirable blocks. Extensive experimental results demonstrate the effectiveness of the proposed searching method and the found efficient super-resolution models achieve better performance than the state-of-the-art methods with limited number of parameters and FLOPs.

The ridges of fingerprint contain enormous discriminative information for fingerprint indexing, however it is hard to depict the structure of ridges for rule-based methods because of nonlinear distortion. This paper investigates to represent the structure of ridges by Deep Convolutional Neural Network (DCNN). The indexing approach partitions the fingerprint image into increasing fine sub-region and extracts feature from each sub-region by DCNN, forming pyramid deep convolutional feature, to represent the global patterns and local details (especially minutiae). Extensive experimental results show that the proposed method achieves better performance on accuracy and efficiency than other prominent indexing approaches. Finally, occlusion sensitivity, visualization and fingerprint reconstruction techniques are employed to explore which attributes of ridges are described in deep convolutional feature.