Novelty Detection
76 papers with code • 0 benchmarks • 0 datasets
Scientific Novelty Detection
Benchmarks
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Libraries
Use these libraries to find Novelty Detection models and implementationsMost implemented papers
Self-supervised network distillation: an effective approach to exploration in sparse reward environments
iskandor/snd
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The solution to such a problem may be to equip the agent with an intrinsic motivation that will provide informed exploration during which the agent is likely to also encounter external reward.
APRIL-GAN: A Zero-/Few-Shot Anomaly Classification and Segmentation Method for CVPR 2023 VAND Workshop Challenge Tracks 1&2: 1st Place on Zero-shot AD and 4th Place on Few-shot AD
In this challenge, our method achieved first place in the zero-shot track, especially excelling in segmentation with an impressive F1 score improvement of 0. 0489 over the second-ranked participant.
EX2: Exploration with Exemplar Models for Deep Reinforcement Learning
Deep reinforcement learning algorithms have been shown to learn complex tasks using highly general policy classes.
Application of SsVGMM to medical data-classification with novelty detection
There is a considerable demand to apply classification in medical analysis.
Confidence from Invariance to Image Transformations
YuvalBahat/Confidence_From_Invariance
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We develop a technique for automatically detecting the classification errors of a pre-trained visual classifier.
q-Space Novelty Detection with Variational Autoencoders
Since abnormal samples are not used during training, we define novelty metrics based on the (partially complementary) assumptions that the VAE is less capable of reconstructing abnormal samples well; that abnormal samples more strongly violate the VAE regularizer; and that abnormal samples differ from normal samples not only in input-feature space, but also in the VAE latent space and VAE output.
Latent Space Autoregression for Novelty Detection
aimagelab/novelty-detection
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CVPR 2019
Novelty detection is commonly referred to as the discrimination of observations that do not conform to a learned model of regularity.
Generative Probabilistic Novelty Detection with Adversarial Autoencoders
podgorskiy/GPND
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NeurIPS 2018
We assume that training data is available to describe only the inlier distribution.
Are generative deep models for novelty detection truly better?
Many deep models have been recently proposed for anomaly detection.
Novelty Goes Deep. A Deep Neural Solution To Document Level Novelty Detection
edithal-14/A-Deep-Neural-Solution-To-Document-Level-Novelty-Detection-COLING-2018-
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COLING 2018
The proposed method outperforms the existing state-of-the-art on a document-level novelty detection dataset by a margin of ∼5{\%} in terms of accuracy.
