A Novel Learnable Orthogonal Wavelet Unit Neural Network with Perfection Reconstruction Constraint Relaxation for Image Classification

CNNs utilize lowpass frequency features, evidenced by max pooling operations that preserve dominant features. Addressing this, we used a one-layer FCN using both coarse and detailed components via DWT. We then developed UwU, a learnable wavelet-based unit that integrates the PR constraint relaxation, allowing feature map component fine-tuning. Distinctively, UwU’s coefficients are trainable, unlike prior studies. This is the first work that utilizes PR constraint relaxation to enhance CNNs. Our innovative techniques serve to enhance stride-convolution, pooling, and downsampling units in CNNs. We tested these improved units using the ResNet family architectures against traditional frequency and wavelet-based units. Performance metrics from CIFAR10, ImageNet1K, and the DTD show promising results. Particularly, while CIFAR10 results are on par with other methods, there’s a marked performance improvement on ImageNet1K and DTD. Further, integrating UwU into the ResNet18-based encoder of the CFLOW-AD system yields competitive anomaly detection results, particularly for hazelnut images in the MVTecAD dataset.