Novelty Detection

72 papers with code • 0 benchmarks • 0 datasets

Scientific Novelty Detection


Use these libraries to find Novelty Detection models and implementations

Most implemented papers

Learning Deep Features for One-Class Classification

PramuPerera/DeepOneClass 16 Jan 2018

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 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

cair/TsetlinMachine 17 Nov 2020

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

cair/TsetlinMachine 10 May 2021

Our approach encodes a description of the novel documents using the linguistic patterns captured by TM clauses.

One-Class Convolutional Neural Network

otkupjnoz/oc-cnn 24 Jan 2019

We present a novel Convolutional Neural Network (CNN) based approach for one class classification.

Generalized Out-of-Distribution Detection: A Survey

jingkang50/oodsurvey 21 Oct 2021

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.

Anomaly Detection via Reverse Distillation from One-Class Embedding

hq-deng/RD4AD CVPR 2022

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.

TAP-DLND 1.0 : A Corpus for Document Level Novelty Detection

edithal-14/A-Deep-Neural-Solution-To-Document-Level-Novelty-Detection-COLING-2018- 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

englhardt/ocs-evaluation 29 Sep 2020

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 23 Jun 2022

In order for artificial agents to successfully perform tasks in changing environments, they must be able to both detect and adapt to novelty.