72 papers with code • 0 benchmarks • 0 datasets
Scientific Novelty Detection
These leaderboards are used to track progress in Novelty Detection
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.
Our approach encodes a description of the novel documents using the linguistic patterns captured by TM clauses.
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.
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.
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.
Our approach is to frame SVDD sampling as an optimization problem, where constraints guarantee that sampling indeed approximates the original decision boundary.
In order for artificial agents to successfully perform tasks in changing environments, they must be able to both detect and adapt to novelty.