# Selective Unsupervised Feature Learning with Convolutional Neural Network (S-CNN)

arXiv: https://arxiv.org/abs/1606.02210 · HTML: https://mlsanity.com/paper/1606.02210/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jun 2016.

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

- [Initialization Using Perlin Noise for Training Networks with a Limited Amount of Data](https://mlsanity.com/paper/2101.07406/) (2021)
- [CG-CNN: Self-Supervised Feature Extraction Through Contextual Guidance and Transfer Learning](https://mlsanity.com/paper/2103.01566/) (2021)

## Abstract (arXiv)

Supervised learning of convolutional neural networks (CNNs) can require very large amounts of labeled data. Labeling thousands or millions of training examples can be extremely time consuming and costly. One direction towards addressing this problem is to create features from unlabeled data. In this paper we propose a new method for training a CNN, with no need for labeled instances. This method for unsupervised feature learning is then successfully applied to a challenging object recognition task. The proposed algorithm is relatively simple, but attains accuracy comparable to that of more sophisticated methods. The proposed method is significantly easier to train, compared to existing CNN methods, making fewer requirements on manually labeled training data. It is also shown to be resistant to overfitting. We provide results on some well-known datasets, namely STL-10, CIFAR-10, and CIFAR-100. The results show that our method provides competitive performance compared with existing alternative methods. Selective Convolutional Neural Network (S-CNN) is a simple and fast algorithm, it introduces a new way to do unsupervised feature learning, and it provides discriminative features which generalize well.