# Hybrid of DiffStride and Spectral Pooling in Convolutional Neural Networks

arXiv: https://arxiv.org/abs/2401.09008 · HTML: https://mlsanity.com/paper/2401.09008/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 16 references that are in the map.
- First posted on arXiv 17 Jan 2024.

## Builds on (most cited first, 15 of 15)

- [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)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Identity Mappings in Deep Residual Networks](https://mlsanity.com/paper/1603.05027/) (2016)
- [Making Convolutional Networks Shift-Invariant Again](https://mlsanity.com/paper/1904.11486/) (2019)
- [Fast Training of Convolutional Networks through FFTs](https://mlsanity.com/paper/1312.5851/) (2013)
- [Spectral Representations for Convolutional Neural Networks](https://mlsanity.com/paper/1506.03767/) (2015)
- [Generalizing Pooling Functions in Convolutional Neural Networks: Mixed, Gated, and Tree](https://mlsanity.com/paper/1509.08985/) (2015)
- [Learning strides in convolutional neural networks](https://mlsanity.com/paper/2202.01653/) (2022)
- [CIFAR10 to Compare Visual Recognition Performance between Deep Neural Networks and Humans](https://mlsanity.com/paper/1811.07270/) (2018)
- [Demystifying Learning Rate Policies for High Accuracy Training of Deep Neural Networks](https://mlsanity.com/paper/1908.06477/) (2019)
- [Overcoming the vanishing gradient problem in plain recurrent networks](https://mlsanity.com/paper/1801.06105/) (2018)
- [Hartley Spectral Pooling for Deep Learning](https://mlsanity.com/paper/1810.04028/) (2018)
- [Deep learning for pedestrians: backpropagation in CNNs](https://mlsanity.com/paper/1811.11987/) (2018)
- [Learning Spatio-Temporal Downsampling for Effective Video Upscaling](https://mlsanity.com/paper/2203.08140/) (2022)

## Abstract (arXiv)

Stride determines the distance between adjacent filter positions as the filter moves across the input. A fixed stride causes important information contained in the image can not be captured, so that important information is not classified. Therefore, in previous research, the DiffStride Method was applied, namely the Strided Convolution Method with which it can learn its own stride value. Severe Quantization and a constraining lower bound on preserved information are arises with Max Pooling Downsampling Method. Spectral Pooling reduce the constraint lower bound on preserved information by cutting off the representation in the frequency domain. In this research a CNN Model is proposed with the Downsampling Learnable Stride Technique performed by Backpropagation combined with the Spectral Pooling Technique. Diffstride and Spectral Pooling techniques are expected to maintain most of the information contained in the image. In this study, we compare the Hybrid Method, which is a combined implementation of Spectral Pooling and DiffStride against the Baseline Method, which is the DiffStride implementation on ResNet 18. The accuracy result of the DiffStride combination with Spectral Pooling improves over DiffStride which is baseline method by 0.0094. This shows that the Hybrid Method can maintain most of the information by cutting of the representation in the frequency domain and determine the stride of the learning result through Backpropagation.