Search Results for author:
Found 5 papers, 4 papers with code
Adversarially Robust Out-of-Distribution Detection Using Lyapunov-Stabilized Embeddings
By incorporating a tailored loss function, we apply Lyapunov stability theory to ensure that both in-distribution (ID) and OOD data converge to stable equilibrium points within the dynamical system.
Universal Novelty Detection Through Adaptive Contrastive Learning
More precisely, for novelty detection, distribution shifts may occur in the training set or the test set.
Seeking Next Layer Neurons' Attention for Error-Backpropagation-Like Training in a Multi-Agent Network Framework
Despite considerable theoretical progress in the training of neural networks viewed as a multi-agent system of neurons, particularly concerning biological plausibility and decentralized training, their applicability to real-world problems remains limited due to scalability issues.
Fake It Till You Make It: Towards Accurate Near-Distribution Novelty Detection
Effectiveness of our method for both the near-distribution and standard novelty detection is assessed through extensive experiments on datasets in diverse applications such as medical images, object classification, and quality control.
Ranked #3 on
Anomaly Detection
on One-class CIFAR-10
(using extra training data)
A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challenges
To date, several research domains tackle the problem of detecting unfamiliar samples, including anomaly detection, novelty detection, one-class learning, open set recognition, and out-of-distribution detection.
