# Multichannel Orthogonal Transform-Based Perceptron Layers for Efficient ResNets Source: [Multichannel Orthogonal Transform-Based Perceptron Layers for Efficient ResNets]() Collection index: [llms.txt]() Data updated: 2025-10-26T02:39:34.909Z Authors: Hongyi Pan, Emadeldeen Hamdan, Xin Zhu, Salih Atici, Ahmet Enis Cetin Published: 2023-03-13T01:07:32.000Z ## Abstract In this paper, we propose a set of transform-based neural network layers as an alternative to the $3\\times3$ Conv2D layers in Convolutional Neural Networks (CNNs). The proposed layers can be implemented based on orthogonal transforms such as the Discrete Cosine Transform (DCT), Hadamard transform (HT), and biorthogonal Block Wavelet Transform (BWT). Furthermore, by taking advantage of the convolution theorems, convolutional filtering operations are performed in the transform domain using element-wise multiplications. Trainable soft-thresholding layers, that remove noise in the transform domain, bring nonlinearity to the transform domain layers. Compared to the Conv2D layer, which is spatial-agnostic and channel-specific, the proposed layers are location-specific and channel-specific. Moreover, these proposed layers reduce the number of parameters and multiplications significantly while improving the accuracy results of regular ResNets on the ImageNet-1K classification task. Furthermore, they can be inserted with a batch normalization layer before the global average pooling layer in the conventional ResNets as an additional layer to improve classification accuracy. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()