Wavelet Integrated CNNs for Noise-Robust Image Classification

Convolutional Neural Networks (CNNs) are generally prone to noise interruptions, i.e., small image noise can cause drastic changes in the output. To suppress the noise effect to the final predication, we enhance CNNs by replacing max-pooling, strided-convolution, and average-pooling with Discrete Wavelet Transform (DWT). We present general DWT and Inverse DWT (IDWT) layers applicable to various wavelets like Haar, Daubechies, and Cohen, etc., and design wavelet integrated CNNs (WaveCNets) using these layers for image classification. In WaveCNets, feature maps are decomposed into the low-frequency and high-frequency components during the down-sampling. The low-frequency component stores main information including the basic object structures, which is transmitted into the subsequent layers to extract robust high-level features. The high-frequency components, containing most of the data noise, are dropped during inference to improve the noise-robustness of the WaveCNets. Our experimental results on ImageNet and ImageNet-C (the noisy version of ImageNet) show that WaveCNets, the wavelet integrated versions of VGG, ResNets, and DenseNet, achieve higher accuracy and better noise-robustness than their vanilla versions.Comment: CVPR accepted pape

First principle study of the thermal conductance in graphene nanoribbon with vacancy and substitutional silicon defect

The thermal conductance in graphene nanoribbon with a vacancy or silicon point defect (substitution of C by Si atom) is investigated by non-equilibrium Green's function (NEGF) formalism combined with first-principle calculations density-functional theory with local density approximation. An efficient correction to the force constant matrix is presented to solve the conflict between the long-range character of the {\it ab initio} approach and the first-nearest-neighboring character of the NEGF scheme. In nanoribbon with a vacancy defect, the thermal conductance is very sensitive to the position of the vacancy defect. A vacancy defect situated at the center of the nanoribbon generates a saddle-like surface, which greatly reduces the thermal conductance by strong scattering to all phonon modes; while an edge vacancy defect only results in a further reconstruction of the edge and slightly reduces the thermal conductance. For the Si defect, the position of the defect plays no role for the value of the thermal conductance, since the defective region is limited within a narrow area around the defect center.Comment: accepted by AP