# LR-Net: A Block-based Convolutional Neural Network for Low-Resolution Image Classification

arXiv: https://arxiv.org/abs/2207.09531 · HTML: https://mlsanity.com/paper/2207.09531/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 28 references that are in the map.
- First posted on arXiv 19 Jul 2022.

## Builds on (most cited first, 18 of 18)

- [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)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Identity Mappings in Deep Residual Networks](https://mlsanity.com/paper/1603.05027/) (2016)
- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [Understanding How Image Quality Affects Deep Neural Networks](https://mlsanity.com/paper/1604.04004/) (2016)
- [Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks](https://mlsanity.com/paper/1512.04143/) (2015)
- [A survey of dimensionality reduction techniques](https://mlsanity.com/paper/1403.2877/) (2014)
- [Contextual Action Recognition with R*CNN](https://mlsanity.com/paper/1505.01197/) (2015)
- [Real-Time Apple Detection System Using Embedded Systems With Hardware Accelerators: An Edge AI Application](https://mlsanity.com/paper/2004.13410/) (2020)
- [Ensemble learning in CNN augmented with fully connected subnetworks](https://mlsanity.com/paper/2003.08562/) (2020)
- [Unsupervised Deep Feature Transfer for Low Resolution Image Classification](https://mlsanity.com/paper/1908.10012/) (2019)
- [Oracle-MNIST: a Dataset of Oracle Characters for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/2205.09442/) (2022)

## Built on by (most cited first, 1 of 1)

- [CATD: Unified Representation Learning for EEG-to-fMRI Cross-Modal Generation](https://mlsanity.com/paper/2408.00777/) (2024)

## Abstract (arXiv)

The success of CNN-based architecture on image classification in learning and extracting features made them so popular these days, but the task of image classification becomes more challenging when we apply state of art models to classify noisy and low-quality images. It is still difficult for models to extract meaningful features from this type of image due to its low-resolution and the lack of meaningful global features. Moreover, high-resolution images need more layers to train which means they take more time and computational power to train. Our method also addresses the problem of vanishing gradients as the layers become deeper in deep neural networks that we mentioned earlier. In order to address all these issues, we developed a novel image classification architecture, composed of blocks that are designed to learn both low level and global features from blurred and noisy low-resolution images. Our design of the blocks was heavily influenced by Residual Connections and Inception modules in order to increase performance and reduce parameter sizes. We also assess our work using the MNIST family datasets, with a particular emphasis on the Oracle-MNIST dataset, which is the most difficult to classify due to its low-quality and noisy images. We have performed in-depth tests that demonstrate the presented architecture is faster and more accurate than existing cutting-edge convolutional neural networks. Furthermore, due to the unique properties of our model, it can produce a better result with fewer parameters.