Modern applications from Autonomous Vehicles to Video Surveillance generate massive amounts of image data. In this work we propose a novel image outlier detection approach (IOD for short) that leverages the cutting-edge image classifier to discover outliers without using any labeled outlier. We observe that although intuitively the confidence that a convolutional neural network (CNN) has that an image belongs to a particular class could serve as outlierness measure to each image, directly applying this confidence to detect outlier does not work well. This is because CNN often has high confidence on an outlier image that does not belong to any target class due to its generalization ability that ensures the high accuracy in classification. To solve this issue, we propose a Deep Neural Forest-based approach that harmonizes the contradictory requirements of accurately classifying images and correctly detecting the outlier images. Our experiments using several benchmark image datasets including MNIST, CIFAR-10, CIFAR-100, and SVHN demonstrate the effectiveness of our IOD approach for outlier detection, capturing more than 90% of outliers generated by injecting one image dataset into another, while still preserving the classification accuracy of the multi-class classification problem. Motivation. As modern applications such as autonomous vehicles and video surveillance generate larger amount of image data, the discovery of outliers from such image data is becoming increasingly critical. Examples of such image outliers include unauthorized personnel observed in a secret military base or unexpected objects encountered by self-driving cars on the road. Capturing these outliers can prevent intelligence leaks or save human lives.State-of-the-Art. Due to the exceptional success of deep learning over classical methods in computer vision, in recent years a number of works BID17 BID27 BID5 BID23 leverage the representation learning ability of a deep autoencoder or GAN BID7 for outlier detection. Outliers are either detected by plugging in the learned representation into classical outlier detection methods or directly reported by employing the reconstruction error as the outlier score BID36 BID4 . However, these approaches use a generic network that is not trained specifically for outlier detection. Although the produced representation is perhaps effective in representing the common features of the "normal" data, it is not necessarily effective in distinguishing "outliers" from "inliers". Recently, some works BID26 BID21 were proposed to solve this issue by incorporating the outlier detection objective actively into the learning process. However, these approaches are all based on the one-class technique BID28 BID18 BID33 that learns a single boundary between outliers and inliers. Although they perform relatively well when handling simplistic data sets such as MNIST, they perform poorly at supporting complex data sets with multiple "normal" classes such as CIFAR-10 ( BID11 ). This is due to the difficulty in finding a separator that encompasses all normal classes yet none of the outliers.Proposed Approach and Contributions. In this work we propose a novel image outlier detection (IOD) strategy that successfully detects image outliers from complex real data sets with multiple normal classes. IOD unifies the core principles of cutting edge deep learning image classifiers BID7 and classical outlier detection within one framework.Classical outlier detection techniques BID3 BID9 BID1 consider an object as an outlier if its outlierness score is above a certain cutoff threshold ct. Intuitively given a Convolutional Neural Network (CNN) BID12 ) trained using normal training data (namely, data without labeled outliers), the confidence that the CNN has that an image belongs to a particular class could be leveraged to measure the outlierness of the image. This is based on the intuition that we expect a CNN to be less confident about an outlier compared to inlier objects, since outliers by definition are dissimilar from any normal class. By using the confidence as an outlier score, IOD could separate outliers from all normal classes. However, our experiments (Sec. 2) show that directly using the confidence produced by CNN to identify outliers in fact is not particularly effective. This is because the requirements of accurately classifying images and correctly detecting the outlier images conflict with each other. CNN achieves high accuracy in image classification because of its excellent generalization capability that enables a CNN to overcome the gap between the training and testing images. However, the generalization capability jeopardizes the detection of outliers, because it increases the chance of erroneously assigning an outlier image to some class with high confidence to which actually it does not fit.We solve this problem by proposing a deep neural decision forest-based (DNDF) approach equipped with an information theory-based regularization function that leverages the strong bias of the classification decisions made within each single decision tree and the ensemble nature of the overall decision forest. Further, we introduce a new architecture of the DNDF that ensures independence amongst the trees and in turn improves the classification accuracy. Finally, we use a joint optimization strategy to train both the spit and leaf nodes of each tree. This speeds up the convergence.We demonstrate the effectiveness of our outlierness measure, the deep neural forest-based approach, the regularization function, and the new architecture using benchmark datasets including MNIST, CIFAR-10, CIFAR-100, and SVHN -with the accuracy higher than 0.9 at detecting outliers, while preserving the accuracy of multi-class classification. In this work we propose a novel approach that effectively detects outliers from image data. The key novelties include a general image outlier detection framework and effective outlierness measure that leverages the deep neural decision forest. Optimizations such as new architecture that connects deep neural network and decision tree and regularization to penalize the large entropy routing decisions are also proposed to further enhance the outlier detection capacity of IOD. In the future we plan to investigate how to make our approach work in multi-label classification setting.
A novel approach that detects outliers from image data,  while preserving the classification accuracy of image classification
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