# One-Class Convolutional Neural Network

arXiv: https://arxiv.org/abs/1901.08688 · HTML: https://mlsanity.com/paper/1901.08688/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jan 2019.

## Built on by (most cited first, 30 of 37)

- [SeeABLE: Soft Discrepancies and Bounded Contrastive Learning for Exposing Deepfakes](https://mlsanity.com/paper/2211.11296/) (2022)
- [One-Class Classification: A Survey](https://mlsanity.com/paper/2101.03064/) (2021)
- [AIROGS: Artificial Intelligence for RObust Glaucoma Screening Challenge](https://mlsanity.com/paper/2302.01738/) (2023)
- [Evaluation of an Audio-Video Multimodal Deepfake Dataset using Unimodal and Multimodal Detectors](https://mlsanity.com/paper/2109.02993/) (2021)
- [Open-set Adversarial Defense with Clean-Adversarial Mutual Learning](https://mlsanity.com/paper/2202.05953/) (2022)
- [Visual Anomaly Detection for Images: A Survey](https://mlsanity.com/paper/2109.13157/) (2021)
- [Learning image representations for anomaly detection: application to discovery of histological alterations in drug development](https://mlsanity.com/paper/2210.07675/) (2022)
- [Anomaly Detection using Edge Computing in Video Surveillance System: Review](https://mlsanity.com/paper/2107.02778/) (2021)
- [Efficient Anomaly Detection Using Self-Supervised Multi-Cue Tasks](https://mlsanity.com/paper/2111.12379/) (2021)
- [The Invariant Ground Truth of Affect](https://mlsanity.com/paper/2210.07630/) (2022)
- [PAD: Towards Principled Adversarial Malware Detection Against Evasion Attacks](https://mlsanity.com/paper/2302.11328/) (2023)
- [Hyp-OC: Hyperbolic One Class Classification for Face Anti-Spoofing](https://mlsanity.com/paper/2404.14406/) (2024)
- [Typing Errors in Factual Knowledge Graphs: Severity and Possible Ways Out](https://mlsanity.com/paper/2102.02307/) (2021)
- [What is Wrong with One-Class Anomaly Detection?](https://mlsanity.com/paper/2104.09793/) (2021)
- [Discriminative-Generative Representation Learning for One-Class Anomaly Detection](https://mlsanity.com/paper/2107.12753/) (2021)
- [Unknown Face Presentation Attack Detection via Localised Learning of Multiple Kernels](https://mlsanity.com/paper/2204.10675/) (2022)
- [Learning The Likelihood Test With One-Class Classifiers for Physical Layer Authentication](https://mlsanity.com/paper/2210.12494/) (2022)
- [Local and Global Information in Obstacle Detection on Railway Tracks](https://mlsanity.com/paper/2307.15478/) (2023)
- [Critical Review for One-class Classification: recent advances and the reality behind them](https://mlsanity.com/paper/2404.17931/) (2024)
- [OOD-SEG: Exploiting out-of-distribution detection techniques for learning image segmentation from sparse multi-class positive-only annotations](https://mlsanity.com/paper/2411.09553/) (2024)
- [Anomaly-Injected Deep Support Vector Data Description for Text Outlier Detection](https://mlsanity.com/paper/2110.14729/) (2021)
- [Unsupervised Deep One-Class Classification with Adaptive Threshold based on Training Dynamics](https://mlsanity.com/paper/2302.06048/) (2023)
- [Introducing 'Inside' Out of Distribution](https://mlsanity.com/paper/2407.04534/) (2024)
- [Chromosomal Structural Abnormality Diagnosis by Homologous Similarity](https://mlsanity.com/paper/2407.08204/) (2024)
- [Tighten The Lasso: A Convex Hull Volume-based Anomaly Detection Method](https://mlsanity.com/paper/2502.18601/) (2025)
- [Interpretable Transformation and Analysis of Timelines through Learning via Surprisability](https://mlsanity.com/paper/2503.04502/) (2025)
- [Learning Unknown Spoof Prompts for Generalized Face Anti-Spoofing Using Only Real Face Images](https://mlsanity.com/paper/2505.03611/) (2025)
- [A Joint Representation Learning and Feature Modeling Approach for One-class Recognition](https://mlsanity.com/paper/2101.09782/) (2021)
- [Projected Sliced Wasserstein Autoencoder-based Hyperspectral Images Anomaly Detection](https://mlsanity.com/paper/2112.11243/) (2021)
- [Improving State-of-the-Art in One-Class Classification by Leveraging Unlabeled Data](https://mlsanity.com/paper/2203.07206/) (2022)

## Abstract (arXiv)

We present a novel Convolutional Neural Network (CNN) based approach for one class classification. The idea is to use a zero centered Gaussian noise in the latent space as the pseudo-negative class and train the network using the cross-entropy loss to learn a good representation as well as the decision boundary for the given class. A key feature of the proposed approach is that any pre-trained CNN can be used as the base network for one class classification. The proposed One Class CNN (OC-CNN) is evaluated on the UMDAA-02 Face, Abnormality-1001, FounderType-200 datasets. These datasets are related to a variety of one class application problems such as user authentication, abnormality detection and novelty detection. Extensive experiments demonstrate that the proposed method achieves significant improvements over the recent state-of-the-art methods. The source code is available at : github.com/otkupjnoz/oc-cnn.