# DCT Perceptron Layer: A Transform Domain Approach for Convolution Layer

arXiv: https://arxiv.org/abs/2211.08577 · HTML: https://mlsanity.com/paper/2211.08577/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 44 references that are in the map.
- First posted on arXiv 15 Nov 2022.

## Builds on (most cited first, 22 of 22)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [Identity Mappings in Deep Residual Networks](https://mlsanity.com/paper/1603.05027/) (2016)
- [CCNet: Criss-Cross Attention for Semantic Segmentation](https://mlsanity.com/paper/1811.11721/) (2018)
- [BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation](https://mlsanity.com/paper/1808.00897/) (2018)
- [Residual Attention Network for Image Classification](https://mlsanity.com/paper/1704.06904/) (2017)
- [Fast-SCNN: Fast Semantic Segmentation Network](https://mlsanity.com/paper/1902.04502/) (2019)
- [Learning Auxiliary Monocular Contexts Helps Monocular 3D Object Detection](https://mlsanity.com/paper/2112.04628/) (2021)
- [Multi-level Wavelet Convolutional Neural Networks](https://mlsanity.com/paper/1907.03128/) (2019)
- [Designing Adaptive Neural Networks for Energy-Constrained Image Classification](https://mlsanity.com/paper/1808.01550/) (2018)
- [Harmonic Convolutional Networks based on Discrete Cosine Transform](https://mlsanity.com/paper/2001.06570/) (2020)
- [Fast Walsh-Hadamard Transform and Smooth-Thresholding Based Binary Layers in Deep Neural Networks](https://mlsanity.com/paper/2104.07085/) (2021)
- [Block Walsh-Hadamard Transform Based Binary Layers in Deep Neural Networks](https://mlsanity.com/paper/2201.02711/) (2022)
- [Compressing the Input for CNNs with the First-Order Scattering Transform](https://mlsanity.com/paper/1809.10200/) (2018)
- [Less is More: Accelerating Faster Neural Networks Straight from JPEG](https://mlsanity.com/paper/2104.00185/) (2021)
- [A Self-Attentive Emotion Recognition Network](https://mlsanity.com/paper/1905.01972/) (2019)
- [Discrete Cosine Transform Based Causal Convolutional Neural Network for Drift Compensation in Chemical Sensors](https://mlsanity.com/paper/2011.06681/) (2020)
- [A Simulation Study to Evaluate the Performance of the Cauchy Proximal Operator in Despeckling SAR Images of the Sea Surface](https://mlsanity.com/paper/2012.06657/) (2020)

## Built on by (most cited first, 3 of 3)

- [ADC/DAC-Free Analog Acceleration of Deep Neural Networks with Frequency Transformation](https://mlsanity.com/paper/2309.01771/) (2023)
- [A novel asymmetrical autoencoder with a sparsifying discrete cosine Stockwell transform layer for gearbox sensor data compression](https://mlsanity.com/paper/2310.02862/) (2023)
- [Containing Analog Data Deluge at Edge through Frequency-Domain Compression in Collaborative Compute-in-Memory Networks](https://mlsanity.com/paper/2309.11048/) (2023)

## Abstract (arXiv)

In this paper, we propose a novel Discrete Cosine Transform (DCT)-based neural network layer which we call DCT-perceptron to replace the 3×3 Conv2D layers in the Residual neural Network (ResNet). Convolutional filtering operations are performed in the DCT domain using element-wise multiplications by taking advantage of the Fourier and DCT Convolution theorems. A trainable soft-thresholding layer is used as the nonlinearity in the DCT perceptron. Compared to ResNet's Conv2D layer which is spatial-agnostic and channel-specific, the proposed layer is location-specific and channel-specific. The DCT-perceptron layer reduces the number of parameters and multiplications significantly while maintaining comparable accuracy results of regular ResNets in CIFAR-10 and ImageNet-1K. Moreover, the DCT-perceptron layer 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.