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We plan to investigate more complex architectures to further improve the accuracy of the proposed HRN method, and exploit the one-class method for positive and unlabeled learning and open-world learning

HRN: A Holistic Approach to One Class Learning

NIPS 2020, (2020)

Cited by: 0|Views48
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Abstract

Existing neural network based one-class learning methods mainly use various forms of auto-encoders or GAN style adversarial training to learn a latent representation of the given one class of data. This paper proposes an entirely different approach based on a novel regularization, called holistic regularization (or H-regularization), whic...More
Introduction
  • One-class learning or classification has many applications. For example, in information retrieval, one has a set of documents of interest and wants to identify more such documents [55].
  • In most applications, deciding whether a data instance belongs to the given training class or is an anomaly can be subjective and a threshold is often used based on the application.
  • Like most existing papers [68, 64, 8, 82], this work is interested in a score function instead, and ignores the above binary decision problem
  • In this case, the commonly used evaluation metric is AUC (Area Under the ROC curve).
  • The authors present the proposed one-class learning method HRN, which uses the above learning paradigm, but employs a novel loss function called one-class loss with an accompanied instance-level data normalization method
Highlights
  • One-class learning or classification has many applications
  • Deep learning models have been proposed for the same purpose [68, 8], which mainly learn a good latent representation of the given
  • Each row in the table gives the average results over 5 runs of all compared systems using one class as the training data for a dataset
  • Existing approaches to one-class learning using deep learning are mainly based on GAN and autoencoders to learn a latent representation of the given class
  • This paper proposed an entirely different approach called HRN, which uses a new one-class loss function with a novel regularization method
  • We plan to investigate more complex architectures to further improve the accuracy of the proposed HRN method, and exploit the one-class method for positive and unlabeled (PU) learning and open-world learning
Methods
  • Method OCSVM iForest DAGMM

    TQM HRN KDDCUP99

    Precision Recall F1

    Thyroid Precision Recall

    Arrhythmia Precision Recall

    Non-Image Datasets: Following the latest baseline TQM [81], the authors use precision, recall and F1 score as the evaluation measures, and apply the same TQM’s thresholding method in HRN, making the precision, recall and F1 scores the same.
  • When our 2-norm instance normalization was added (NLL+Hreg+2N_Inst_Norm), the results were improved even further.
  • The instance normalization method in [79] (NLL+Hreg+Inst_Norm[79]) did fairly well, but it is significantly poorer than HRN (NLL+Hreg+2N_Inst_Norm).
  • This is because that the method in [79] does not solve the problem identified in Sec. 3.2.
  • When each new class comes, it is incrementally learned using the one-class loss (Sec. 3.1)
Results
  • Results and Discussion

    Image Datasets: The authors first report the results on the three image datasets and the three non-image datasets.
  • Each row in the table gives the average results over 5 runs of all compared systems using one class as the training data for a dataset.
  • Its AUC is only 52.10 while the HRN’s AUC is 71.32
  • This dataset was not used in the experimental evaluation of the TQM paper.
  • Its average AUC on the CIFAR-10 dataset is much better than other baselines, it is still markedly lower than HRN.
Conclusion
  • Existing approaches to one-class learning using deep learning are mainly based on GAN and autoencoders to learn a latent representation of the given class.
  • This paper proposed an entirely different approach called HRN, which uses a new one-class loss function with a novel regularization method.
  • The architecture of HRN is very simple.
  • The authors plan to investigate more complex architectures to further improve the accuracy of the proposed HRN method, and exploit the one-class method for PU learning and open-world learning
Tables
  • Table1: Average AUCs in % over 5 runs per method on the three image datasets
  • Table2: Average precision, recall, and F1 score on the three non-image datasets over 5 runs
  • Table3: Average AUCs in % of different components of HRN on the image datasets. Hreg: H-regularization; 2N_Inst_Norm: our 2-norm instance normalization; Inst_Norm[<a class="ref-link" id="c79" href="#r79">79</a>]: instance normalization in [<a class="ref-link" id="c79" href="#r79">79</a>]; SquareLoss: Square Loss
  • Table4: Average AUCs in % on CIFAR-10: Pre-training using ImageNet without overlapping classes
  • Table5: Average AUCs in % with different training data noise ratio for HRN
  • Table6: Average AUCs in % per method on GTSRB stop sign adversarial attacks
  • Table7: Continual learning accuracy results for 1 class per task of HCL and the baselines
Download tables as Excel
Related work
  • Much of the existing work on anomaly, outlier or novelty detection can be regarded as some form of one-class learning from a class of normal data. Early work in statistics [4] was mainly based on probabilistic modeling of the distribution of the normal data and regard data points with low probabilities in the distribution as anomalies [4, 87, 22, 86]. In general, anomaly detection algorithms can be classified into the following categories: distance based methods [42, 3, 28, 31, 60], density based methods [38, 56, 6], mixture models [3, 43], one-class classification based methods [75, 78, 39], deep learning based representation learning using auto-encoders [10, 89, 69, 7, 94] and adversarial learning [74, 15, 64, 20], ensemble methods [53, 10], graphs and random walks [58, 31], transfer learning [45, 2], and multi-task learning [35]. Several surveys have also been published [9, 66, 7, 61].

    About one-class learning, one-class SVM (OCSVM) [75] was perhaps the earliest method, which uses the kernel SVM to separate the data from the origin. It essentially treats the origin as the only negative data point. Another earlier method based on kernel SVM is the Support Vector Data Description (SVDD) [78], which tries to find a hypersphere to enclose the given class of data. [21] learns features using deep learning and then applies OCSVM or SVDD to build the one-class model.
Funding
  • Acknowledgments and Disclosure of Funding This work was partially supported by the National Key Research and Development Program of China under grant 2018AAA0100205
Study subjects and analysis
datasets: 3
Table 3 gives the ablation results of MNIST, fMNIST and CIFAR-10. We see that using only NLL, the model performed very poorly on all three datasets. Including H-regularization (NLL+Hreg) improves the performance drastically

image classification datasets: 3
To our knowledge, both H-regularization and the normalization method have not been reported in the literature. Empirical evaluation using three image classification datasets widely used in evaluating one-class learners and three traditional benchmark anomaly detection datasets demonstrates the effectiveness of HRN. It outperforms eleven state-of-the-art baselines considerably

benchmark datasets: 6
4 Empirical Evaluation. We empirically evaluate the proposed algorithm HRN using six benchmark datasets and eleven state-of-the-art baselines. Following existing papers, no pre-trained feature extractors were used in the main evaluation

benchmark image classification datasets: 3
Datasets. We use three benchmark image classification datasets and three benchmark traditional nonimage anomaly detection datasets that have been used in many previous papers. (1) MNIST [47]6 is a handwritten digit classification dataset of 10 digits, i.e., 10 classes

image datasets: 3
(3) CIFAR-10 [44]8 is also an image classification dataset consisting of 60,000 32x32 color images of 10 classes with the splitting of 50,000 for training and 10,000 for testing. For each of these three image datasets, we use the training data of each class C in the dataset in turn as the one class data to build a model and then test the model using the full test set of all classes. The rest of the classes except C are anomalies

non-image datasets: 3
The rest of the classes except C are anomalies. The three non-image datasets are: (4) KDDCUP99 9 consists of 450000 training instances and 44021 test instances of two classes. The majority class (80% of the data) is regarded as the one class used in learning

datasets: 5
We followed the TQM approach and used grid search. However, we used only the MNIST data to search for hyper-parameter values and then applied the values to all 5 datasets. Grid search uses the following tuning ranges: for λ, from 0 to 1 with step 0.05 and for n, from 1 to 20 with step 1

image datasets: 3
4.3 Results and Discussion. Image Datasets: We first report the results on the three image datasets and then the three non-image datasets. The main results on the three image datasets are given in Table 1.13 AUC (Area Under the ROC curve) is the evaluation metric, which is also used in most one-class and other anomaly detection algorithms [7, 64, 68]

image datasets: 3
Image Datasets: We first report the results on the three image datasets and then the three non-image datasets. The main results on the three image datasets are given in Table 1.13 AUC (Area Under the ROC curve) is the evaluation metric, which is also used in most one-class and other anomaly detection algorithms [7, 64, 68]. Each row in the table gives the average results over 5 runs of all compared systems using one class (column 1) as the training data for a dataset

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Author
Mengyu Wang
Mengyu Wang
Qi Qin
Qi Qin
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