193 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
How important are specialized transforms in Neural Operators?
Simulating physical systems using Partial Differential Equations (PDEs) has
become an indispensible part of modern industrial process optimization.
Traditionally, numerical solvers have been used to solve the associated PDEs,
however recently Transform-based Neural Operators such as the Fourier Neural
Operator and Wavelet Neural Operator have received a lot of attention for their
potential to provide fast solutions for systems of PDEs. In this work, we
investigate the importance of the transform layers to the reported success of
transform based neural operators. In particular, we record the cost in terms of
performance, if all the transform layers are replaced by learnable linear
layers. Surprisingly, we observe that linear layers suffice to provide
performance comparable to the best-known transform-based layers and seem to do
so with a compute time advantage as well. We believe that this observation can
have significant implications for future work on Neural Operators, and might
point to other sources of efficiencies for these architectures.Comment: 8 pages, 3 figures, 4 table
- …