Novelty Detection
76 papers with code • 0 benchmarks • 0 datasets
Scientific Novelty Detection
Benchmarks
These leaderboards are used to track progress in Novelty Detection
Libraries
Use these libraries to find Novelty Detection models and implementationsMost implemented papers
Learning Deep Features for One-Class Classification
We propose a deep learning-based solution for the problem of feature learning in one-class classification.
Adversarially Learned One-Class Classifier for Novelty Detection
khalooei/ALOCC-CVPR2018
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CVPR 2018
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.
Measuring the Novelty of Natural Language Text Using the Conjunctive Clauses of a Tsetlin Machine Text Classifier
The mechanism uses the conjunctive clauses of the TM to measure to what degree a text matches the classes covered by the training data.
Word-level Human Interpretable Scoring Mechanism for Novel Text Detection Using Tsetlin Machines
Our approach encodes a description of the novel documents using the linguistic patterns captured by TM clauses.
Anomaly Detection via Reverse Distillation from One-Class Embedding
Knowledge distillation (KD) achieves promising results on the challenging problem of unsupervised anomaly detection (AD). The representation discrepancy of anomalies in the teacher-student (T-S) model provides essential evidence for AD.
One-Class Convolutional Neural Network
otkupjnoz/oc-cnn
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We present a novel Convolutional Neural Network (CNN) based approach for one class classification.
Generalized Out-of-Distribution Detection: A Survey
In this survey, we first present a unified framework called generalized OOD detection, which encompasses the five aforementioned problems, i. e., AD, ND, OSR, OOD detection, and OD.
TAP-DLND 1.0 : A Corpus for Document Level Novelty Detection
edithal-14/A-Deep-Neural-Solution-To-Document-Level-Novelty-Detection-COLING-2018-
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LREC 2018
Detecting novelty of an entire document is an Artificial Intelligence (AI) frontier problem that has widespread NLP applications, such as extractive document summarization, tracking development of news events, predicting impact of scholarly articles, etc.
Efficient SVDD Sampling with Approximation Guarantees for the Decision Boundary
Our approach is to frame SVDD sampling as an optimization problem, where constraints guarantee that sampling indeed approximates the original decision boundary.
NovelCraft: A Dataset for Novelty Detection and Discovery in Open Worlds
tufts-ai-robotics-group/polycraft-novelty-data
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In order for artificial agents to successfully perform tasks in changing environments, they must be able to both detect and adapt to novelty.
