132 research outputs found
Restricted Generative Projection for One-Class Classification and Anomaly Detection
We present a simple framework for one-class classification and anomaly
detection. The core idea is to learn a mapping to transform the unknown
distribution of training (normal) data to a known target distribution.
Crucially, the target distribution should be sufficiently simple, compact, and
informative. The simplicity is to ensure that we can sample from the
distribution easily, the compactness is to ensure that the decision boundary
between normal data and abnormal data is clear and reliable, and the
informativeness is to ensure that the transformed data preserve the important
information of the original data. Therefore, we propose to use truncated
Gaussian, uniform in hypersphere, uniform on hypersphere, or uniform between
hyperspheres, as the target distribution. We then minimize the distance between
the transformed data distribution and the target distribution while keeping the
reconstruction error for the original data small enough. Comparative studies on
multiple benchmark datasets verify the effectiveness of our methods in
comparison to baselines
Self-Discriminative Modeling for Anomalous Graph Detection
This paper studies the problem of detecting anomalous graphs using a machine
learning model trained on only normal graphs, which has many applications in
molecule, biology, and social network data analysis. We present a
self-discriminative modeling framework for anomalous graph detection. The key
idea, mathematically and numerically illustrated, is to learn a discriminator
(classifier) from the given normal graphs together with pseudo-anomalous graphs
generated by a model jointly trained, where we never use any true anomalous
graphs and we hope that the generated pseudo-anomalous graphs interpolate
between normal ones and (real) anomalous ones. Under the framework, we provide
three algorithms with different computational efficiencies and stabilities for
anomalous graph detection. The three algorithms are compared with several
state-of-the-art graph-level anomaly detection baselines on nine popular graph
datasets (four with small size and five with moderate size) and show
significant improvement in terms of AUC. The success of our algorithms stems
from the integration of the discriminative classifier and the well-posed
pseudo-anomalous graphs, which provide new insights for anomaly detection.
Moreover, we investigate our algorithms for large-scale imbalanced graph
datasets. Surprisingly, our algorithms, though fully unsupervised, are able to
significantly outperform supervised learning algorithms of anomalous graph
detection. The corresponding reason is also analyzed.Comment: This work was submitted to NeurIPS 2023 but was unfortunately
rejecte
Learning Large Margin Sparse Embeddings for Open Set Medical Diagnosis
Fueled by deep learning, computer-aided diagnosis achieves huge advances.
However, out of controlled lab environments, algorithms could face multiple
challenges. Open set recognition (OSR), as an important one, states that
categories unseen in training could appear in testing. In medical fields, it
could derive from incompletely collected training datasets and the constantly
emerging new or rare diseases. OSR requires an algorithm to not only correctly
classify known classes, but also recognize unknown classes and forward them to
experts for further diagnosis. To tackle OSR, we assume that known classes
could densely occupy small parts of the embedding space and the remaining
sparse regions could be recognized as unknowns. Following it, we propose Open
Margin Cosine Loss (OMCL) unifying two mechanisms. The former, called Margin
Loss with Adaptive Scale (MLAS), introduces angular margin for reinforcing
intra-class compactness and inter-class separability, together with an adaptive
scaling factor to strengthen the generalization capacity. The latter, called
Open-Space Suppression (OSS), opens the classifier by recognizing sparse
embedding space as unknowns using proposed feature space descriptors. Besides,
since medical OSR is still a nascent field, two publicly available benchmark
datasets are proposed for comparison. Extensive ablation studies and feature
visualization demonstrate the effectiveness of each design. Compared with
state-of-the-art methods, MLAS achieves superior performances, measured by ACC,
AUROC, and OSCR
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