Search Results for author:
Found 3 papers, 2 papers with code
C3: Cross-instance guided Contrastive Clustering
In this paper, we propose a novel contrastive clustering method, Cross-instance guided Contrastive Clustering (C3), that considers the cross-sample relationships to increase the number of positive pairs and mitigate the impact of false negative, noise, and anomaly sample on the learned representation of data.
Ranked #4 on
Image Clustering
on Tiny-ImageNet
Self-Supervised Anomaly Detection in Computer Vision and Beyond: A Survey and Outlook
Anomaly detection (AD) plays a crucial role in various domains, including cybersecurity, finance, and healthcare, by identifying patterns or events that deviate from normal behaviour.
Self-Supervised Anomaly Detection
Self-Supervised Learning
+1
DASVDD: Deep Autoencoding Support Vector Data Descriptor for Anomaly Detection
In this paper, we propose a method, DASVDD, that jointly learns the parameters of an autoencoder while minimizing the volume of an enclosing hyper-sphere on its latent representation.
Ranked #7 on
Anomaly Detection
on Fashion-MNIST
