9,714 research outputs found
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
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