Dilated Deep Residual Network for Image Denoising

Variations of deep neural networks such as convolutional neural network (CNN) have been successfully applied to image denoising. The goal is to automatically learn a mapping from a noisy image to a clean image given training data consisting of pairs of noisy and clean images. Most existing CNN models for image denoising have many layers. In such cases, the models involve a large amount of parameters and are computationally expensive to train. In this paper, we develop a dilated residual CNN for Gaussian image denoising. Compared with the recently proposed residual denoiser, our method can achieve comparable performance with less computational cost. Specifically, we enlarge receptive field by adopting dilated convolution in residual network, and the dilation factor is set to a certain value. We utilize appropriate zero padding to make the dimension of the output the same as the input. It has been proven that the expansion of receptive field can boost the CNN performance in image classification, and we further demonstrate that it can also lead to competitive performance for denoising problem. Moreover, we present a formula to calculate receptive field size when dilated convolution is incorporated. Thus, the change of receptive field can be interpreted mathematically. To validate the efficacy of our approach, we conduct extensive experiments for both gray and color image denoising with specific or randomized noise levels. Both of the quantitative measurements and the visual results of denoising are promising comparing with state-of-the-art baselines.Comment: camera ready, 8 pages, accepted to IEEE ICTAI 201

Sipping Fuel and Saving Lives: Increasing Fuel Economy without Sacrificing Safety

Demonstrates how new fuel-efficiency technologies make it possible, and advisable, to significantly increase the fuel economy of motor vehicles without compromising their safety

The Patent Spiral

Examination — the process of reviewing a patent application and deciding whether to grant the requested patent — improves patent quality in two ways. It acts as a substantive screen, filtering out meritless applications and improving meritorious ones. It also acts as a costly screen, discouraging applicants from seeking low-value patents. Yet despite these dual roles, the patent system has a substantial quality problem: it is both too easy to get a patent (because examiners grant invalid patents that should be filtered out by a substantive screen) and too cheap to do so (because examiners grant low-value nuisance patents that should be filtered out by a costly screen). This Article argues that these flaws in patent screening are both worse and better than has been recognized. The flaws are worse because they are not static, but dynamic, interacting to reinforce each other. This interaction leads to a vicious cycle of more and more patents that should never have been granted. When patents are too easily obtained, that undermines the costly screen, because even a plainly invalid patent has a nuisance value greater than its cost. And when patents are too cheaply obtained, that undermines the substantive screen, because there will be more patent applications, and the examination system cannot scale indefinitely without sacrificing accuracy. The result is a cycle of more and more applications, being screened less and less accurately, to give more and more low-quality patents. And although it is hard to test directly if the quality of patent examination is falling, there is evidence suggesting that this cycle is affecting the patent system. At the same time, these flaws are not as bad as they seem because this cycle may be surprisingly easy to solve. The cycle gives policymakers substantial flexibility in designing patent reforms, because the effect of a reform on one piece of the cycle will propagate to the rest of the cycle. Reformers can concentrate on the easiest places to make reforms (like the litigation system) instead of trying to do the impossible (like eliminating examination errors). Such reforms would not only have local effects, but could help make the entire patent system work better

CondenseNet: An Efficient DenseNet using Learned Group Convolutions

Deep neural networks are increasingly used on mobile devices, where computational resources are limited. In this paper we develop CondenseNet, a novel network architecture with unprecedented efficiency. It combines dense connectivity with a novel module called learned group convolution. The dense connectivity facilitates feature re-use in the network, whereas learned group convolutions remove connections between layers for which this feature re-use is superfluous. At test time, our model can be implemented using standard group convolutions, allowing for efficient computation in practice. Our experiments show that CondenseNets are far more efficient than state-of-the-art compact convolutional networks such as MobileNets and ShuffleNets