# Unsupervised Deep Feature Transfer for Low Resolution Image Classification

arXiv: https://arxiv.org/abs/1908.10012 · HTML: https://mlsanity.com/paper/1908.10012/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Aug 2019.

## Built on by (most cited first, 5 of 5)

- [Colonoscopy Polyp Detection and Classification: Dataset Creation and Comparative Evaluations](https://mlsanity.com/paper/2104.10824/) (2021)
- [Six-channel Image Representation for Cross-domain Object Detection](https://mlsanity.com/paper/2101.00561/) (2021)
- [LR-Net: A Block-based Convolutional Neural Network for Low-Resolution Image Classification](https://mlsanity.com/paper/2207.09531/) (2022)
- [Training Deep Neural Networks via Branch-and-Bound](https://mlsanity.com/paper/2104.01730/) (2021)
- [Collaborative Synthetic Data Generation for Knowledge Transfer in Federated Learning](https://mlsanity.com/paper/2607.07565/) (2026)

## Abstract (arXiv)

In this paper, we propose a simple while effective unsupervised deep feature transfer algorithm for low resolution image classification. No fine-tuning on convenet filters is required in our method. We use pre-trained convenet to extract features for both high- and low-resolution images, and then feed them into a two-layer feature transfer network for knowledge transfer. A SVM classifier is learned directly using these transferred low resolution features. Our network can be embedded into the state-of-the-art deep neural networks as a plug-in feature enhancement module. It preserves data structures in feature space for high resolution images, and transfers the distinguishing features from a well-structured source domain (high resolution features space) to a not well-organized target domain (low resolution features space). Extensive experiments on VOC2007 test set show that the proposed method achieves significant improvements over the baseline of using feature extraction.