# Connective Reconstruction-based Novelty Detection

arXiv: https://arxiv.org/abs/2210.13917 · HTML: https://mlsanity.com/paper/2210.13917/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 36 references that are in the map.
- First posted on arXiv 25 Oct 2022.

## Builds on (most cited first, 12 of 12)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery](https://mlsanity.com/paper/1703.05921/) (2017)
- [GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training](https://mlsanity.com/paper/1805.06725/) (2018)
- [Adversarially Learned One-Class Classifier for Novelty Detection](https://mlsanity.com/paper/1802.09088/) (2018)
- [Adversarially Learned Anomaly Detection](https://mlsanity.com/paper/1812.02288/) (2018)
- [Anomaly Detection using One-Class Neural Networks](https://mlsanity.com/paper/1802.06360/) (2018)
- [G2D: Generate to Detect Anomaly](https://mlsanity.com/paper/2006.11629/) (2020)
- [Improved anomaly detection by training an autoencoder with skip connections on images corrupted with Stain-shaped noise](https://mlsanity.com/paper/2008.12977/) (2020)
- [GAN Ensemble for Anomaly Detection](https://mlsanity.com/paper/2012.07988/) (2020)
- [Provable Self-Representation Based Outlier Detection in a Union of Subspaces](https://mlsanity.com/paper/1704.03925/) (2017)
- [Attribute Restoration Framework for Anomaly Detection](https://mlsanity.com/paper/1911.10676/) (2019)
- [OLED: One-Class Learned Encoder-Decoder Network with Adversarial Context Masking for Novelty Detection](https://mlsanity.com/paper/2103.14953/) (2021)

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

- [An Exploration of Default Images in Text-to-Image Generation](https://mlsanity.com/paper/2505.09166/) (2025)

## Abstract (arXiv)

Detection of out-of-distribution samples is one of the critical tasks for real-world applications of computer vision. The advancement of deep learning has enabled us to analyze real-world data which contain unexplained samples, accentuating the need to detect out-of-distribution instances more than before. GAN-based approaches have been widely used to address this problem due to their ability to perform distribution fitting; however, they are accompanied by training instability and mode collapse. We propose a simple yet efficient reconstruction-based method that avoids adding complexities to compensate for the limitations of GAN models while outperforming them. Unlike previous reconstruction-based works that only utilize reconstruction error or generated samples, our proposed method simultaneously incorporates both of them in the detection task. Our model, which we call "Connective Novelty Detection" has two subnetworks, an autoencoder, and a binary classifier. The autoencoder learns the representation of the positive class by reconstructing them. Then, the model creates negative and connected positive examples using real and generated samples. Negative instances are generated via manipulating the real data, so their distribution is close to the positive class to achieve a more accurate boundary for the classifier. To boost the robustness of the detection to reconstruction error, connected positive samples are created by combining the real and generated samples. Finally, the binary classifier is trained using connected positive and negative examples. We demonstrate a considerable improvement in novelty detection over state-of-the-art methods on MNIST and Caltech-256 datasets.