# Deconvolution-and-convolution Networks

arXiv: https://arxiv.org/abs/2103.11887 · HTML: https://mlsanity.com/paper/2103.11887/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 38 references that are in the map.
- First posted on arXiv 22 Mar 2021.

## Builds on (most cited first, 11 of 11)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [End-to-End Training of Deep Visuomotor Policies](https://mlsanity.com/paper/1504.00702/) (2015)
- [Learning Deconvolution Network for Semantic Segmentation](https://mlsanity.com/paper/1505.04366/) (2015)
- [1D Convolutional Neural Networks and Applications: A Survey](https://mlsanity.com/paper/1905.03554/) (2019)
- [End-to-end representation learning for Correlation Filter based tracking](https://mlsanity.com/paper/1704.06036/) (2017)
- [Parameterized Machine Learning for High-Energy Physics](https://mlsanity.com/paper/1601.07913/) (2016)
- [Sequence-to-Sequence Speech Recognition with Time-Depth Separable Convolutions](https://mlsanity.com/paper/1904.02619/) (2019)
- [A practical tutorial on autoencoders for nonlinear feature fusion: Taxonomy, models, software and guidelines](https://mlsanity.com/paper/1801.01586/) (2018)
- [Convolutional Recurrent Neural Networks for Electrocardiogram Classification](https://mlsanity.com/paper/1710.06122/) (2017)
- [Deep Networks with Fast Retraining](https://mlsanity.com/paper/2008.07387/) (2020)

## Built on by (most cited first, 2 of 2)

- [Physics-Informed Computer Vision: A Review and Perspectives](https://mlsanity.com/paper/2305.18035/) (2023)
- [Attention-effective multiple instance learning on weakly stem cell colony segmentation](https://mlsanity.com/paper/2203.04606/) (2022)

## Abstract (arXiv)

2D Convolutional neural network (CNN) has arguably become the de facto standard for computer vision tasks. Recent findings, however, suggest that CNN may not be the best option for 1D pattern recognition, especially for datasets with over 1 M training samples, e.g., existing CNN-based methods for 1D signals are highly reliant on human pre-processing. Common practices include utilizing discrete Fourier transform (DFT) to reconstruct 1D signal into 2D array. To add to extant knowledge, in this paper, a novel 1D data processing algorithm is proposed for 1D big data analysis through learning a deep deconvolutional-convolutional network. Rather than resorting to human-based techniques, we employed deconvolution layers to convert 1 D signals into 2D data. On top of the deconvolution model, the data was identified by a 2D CNN. Compared with the existing 1D signal processing algorithms, DCNet boasts the advantages of less human-made inference and higher generalization performance. Our experimental results from a varying number of training patterns (50 K to 11 M) from classification and regression demonstrate the desirability of our new approach.