Search Results for author:
Found 5 papers, 2 papers with code
Feature Decoupling in Self-supervised Representation Learning for Open Set Recognition
In the first stage, we introduce a self-supervised feature decoupling method that finds the content features of the input samples from the known classes.
Representation learning with function call graph transformations for malware open set recognition
In this paper, we introduce a self-supervised pre-training approach for the OSR problem in malware classification.
Self-supervised Detransformation Autoencoder for Representation Learning in Open Set Recognition
The objective of Open set recognition (OSR) is to learn a classifier that can reject the unknown samples while classifying the known classes accurately.
MMF: A loss extension for feature learning in open set recognition
Our contributions include: First, we introduce an extension that can be incorporated into different loss functions to find more discriminative representations.
Learning a Neural-network-based Representation for Open Set Recognition
Open set recognition problems exist in many domains.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
