Novelty Detection
77 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
Metric Learning for Novelty and Anomaly Detection
When neural networks process images which do not resemble the distribution seen during training, so called out-of-distribution images, they often make wrong predictions, and do so too confidently.
Hierarchy-based Image Embeddings for Semantic Image Retrieval
cvjena/semantic-embeddings
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Such an embedding does not only improve image retrieval results, but could also facilitate integrating semantics for other tasks, e. g., novelty detection or few-shot learning.
Neural Processes Mixed-Effect Models for Deep Normative Modeling of Clinical Neuroimaging Data
smkia/DNM
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Normative modeling has recently been introduced as a promising approach for modeling variation of neuroimaging measures across individuals in order to derive biomarkers of psychiatric disorders.
Recurrent Neural Networks with Stochastic Layers for Acoustic Novelty Detection
dnguyengithub/AudioNovelty
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In this paper, we adapt Recurrent Neural Networks with Stochastic Layers, which are the state-of-the-art for generating text, music and speech, to the problem of acoustic novelty detection.
Deep Transfer Learning for Multiple Class Novelty Detection
PramuPerera/TransferLearningNovelty
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CVPR 2019
We show that thresholding the maximal activation of the proposed network can be used to identify novel objects effectively.
Unsupervised Progressive Learning and the STAM Architecture
We first pose the Unsupervised Progressive Learning (UPL) problem: an online representation learning problem in which the learner observes a non-stationary and unlabeled data stream, learning a growing number of features that persist over time even though the data is not stored or replayed.
Deep Unknown Intent Detection with Margin Loss
With margin loss, we can learn discriminative deep features by forcing the network to maximize inter-class variance and to minimize intra-class variance.
Outside the Box: Abstraction-Based Monitoring of Neural Networks
Neural networks have demonstrated unmatched performance in a range of classification tasks.
Multivariate Triangular Quantile Maps for Novelty Detection
GinGinWang/MTQ
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NeurIPS 2019
Novelty detection, a fundamental task in machine learning, has drawn a lot of recent attention due to its wide-ranging applications and the rise of neural approaches.
Continual egocentric object recognition
lucaerculiani/towards-visual-semantics
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We present a framework capable of tackilng the problem of continual object recognition in a setting which resembles that under whichhumans see and learn.
