Efficient Transform-Based Perceptron Layers for Improved ResNet Performance

The authors propose a set of transform-based neural network layers as an alternative to the 3x3 Conv2D layers in Convolutional Neural Networks (CNNs). These layers can be implemented based on orthogonal transforms such as the Discrete Cosine Transform (DCT), Hadamard transform (HT), and biorthogonal Block Wavelet Transform (BWT). The proposed layers reduce the number of parameters and multiplications significantly while improving the accuracy results of regular ResNets on the ImageNet-1K classification task.