Adversarially Learned One-Class Classifier for Novelty Detection (1802.09088v2)

Abstract: Novelty detection is the process of identifying the observation(s) that differ in some respect from the training observations (the target class). In reality, the novelty class is often absent during training, poorly sampled or not well defined. Therefore, one-class classifiers can efficiently model such problems. However, due to the unavailability of data from the novelty class, training an end-to-end deep network is a cumbersome task. In this paper, inspired by the success of generative adversarial networks for training deep models in unsupervised and semi-supervised settings, we propose an end-to-end architecture for one-class classification. Our architecture is composed of two deep networks, each of which trained by competing with each other while collaborating to understand the underlying concept in the target class, and then classify the testing samples. One network works as the novelty detector, while the other supports it by enhancing the inlier samples and distorting the outliers. The intuition is that the separability of the enhanced inliers and distorted outliers is much better than deciding on the original samples. The proposed framework applies to different related applications of anomaly and outlier detection in images and videos. The results on MNIST and Caltech-256 image datasets, along with the challenging UCSD Ped2 dataset for video anomaly detection illustrate that our proposed method learns the target class effectively and is superior to the baseline and state-of-the-art methods.

• The paper introduces a dual-network structure where a reconstructor and a discriminator work together to perform one-class novelty detection without outlier data.
• It leverages GAN-like adversarial training to enhance the separability between inliers and outliers, achieving improved F1-scores and AUC metrics.
• Validated on datasets like MNIST and UCSD Ped2, the approach demonstrates robust performance in real-world image and video anomaly detection tasks.

Adversarially Learned One-Class Classifier for Novelty Detection: An Overview

The paper, "Adversarially Learned One-Class Classifier for Novelty Detection," presents a novel approach to tackle the problem of novelty detection using an adversarial learning framework. This approach leverages the strengths of Generative Adversarial Networks (GANs) to enable one-class classification without requiring samples from the novelty or outlier class during training.

Theoretical Contributions

The authors introduce an end-to-end architecture consisting of two deep networks: a reconstructor, denoted as $\mathcal{R}$, and a discriminator, denoted as $\mathcal{D}$. The reconstructor is trained to refine or reconstruct input samples to resemble inliers and distort outliers, while the discriminator identifies whether a sample belongs to the target class.

The framework is designed to learn the distribution of inlier samples through adversarial training, mimicking the unsupervised capabilities of GANs but applied to a one-class setting. Importantly, this paper addresses the challenge of modeling the target class without samples from the novelty class, which is often unavailable or poorly defined in realistic scenarios.

Results and Evaluation

The authors demonstrate their method's effectiveness on multiple image and video datasets, including MNIST and UCSD Ped2. They report superior performance compared to baseline methods across varied setups, notably in environments with a high proportion of outlier data.

For the MNIST dataset, the model shows robust results even as the proportion of outlier samples increases. Furthermore, on the challenging UCSD Ped2 dataset, the approach yields competitive frame-level anomaly detection results, indicating its applicability to real-world video anomaly detection tasks.

Numerical Insights

Consistent with prior art, the use of $\mathcal{R}$ enhances the ability of $\mathcal{D}$ by improving the separability between inliers and outliers. The inclusion of noise robustness during training further strengthens the model's ability to generalize to unseen data distributions.

The numerical results highlight that, while $\mathcal{D}$ alone outperforms existing strategies, the combination of $\mathcal{R}$ and $\mathcal{D}$ excels in distinguishing inliers from outliers. The evaluations on image datasets show improvements in $F_1$-scores and AUC metrics, underlining the method's efficacy.

Implications and Future Directions

This adversarially learned one-class classifier presents significant implications for fields involving anomaly and outlier detection. Its ability to function effectively without requiring novelty samples during training makes it particularly valuable where anomalies are rare or costly to obtain.

Future work could explore optimizing the configuration of the neural networks to further enhance performance or reduce computational complexity. Additionally, extending the framework to incorporate temporal features could refine its effectiveness in video anomaly detection.

By providing a robust and adaptable approach to novelty detection, this work contributes a meaningful advancement in the ongoing development of deep learning models for real-time and large-scale anomaly detection tasks. As these applications continue to evolve, frameworks like the one proposed here will be foundational in shaping responsive and intelligent systems.