mlsanityData to 7 Oct 2026

Hybrid of DiffStride and Spectral Pooling in Convolutional Neural Networks

arXiv 2401.09008 ↗First posted 17 Jan 2024cs.CV cs.AIcited by 0

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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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 15 of its 16 references that are in the map. First posted on arXiv 17 Jan 2024.

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15 of 16 references.

References · 15

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  10. 2018CIFAR10 to Compare Visual Recognition Performance between Deep Neural Networks and Humanscited by 14
  11. 2019Demystifying Learning Rate Policies for High Accuracy Training of Deep Neural Networkscited by 12
  12. 2018Overcoming the vanishing gradient problem in plain recurrent networkscited by 10
3 more
  1. 2018Hartley Spectral Pooling for Deep Learningcited by 6
  2. 2018Deep learning for pedestrians: backpropagation in CNNscited by 3
  3. 2022Learning Spatio-Temporal Downsampling for Effective Video Upscalingcited by 2

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