8 citations · 11 across the 5 of their papers we have counts for
5 papers
Feature Decoupling in Self-supervised Representation Learning for Open Set Recognition
Jingyun Jia, Philip K. Chan
Assuming unknown classes could be present during classification, the open set recognition (OSR) task aims to classify an instance into a known class or reject it as unknown. In thi…
Representation learning with function call graph transformations for malware open set recognition
Jingyun Jia, Philip K. Chan
Open set recognition (OSR) problem has been a challenge in many machine learning (ML) applications, such as security. As new/unknown malware families occur regularly, it is difficu…
Self-supervised Detransformation Autoencoder for Representation Learning in Open Set Recognition
Jingyun Jia, Philip K. Chan
The objective of Open set recognition (OSR) is to learn a classifier that can reject the unknown samples while classifying the known classes accurately. In this paper, we propose a…
MMF: A loss extension for feature learning in open set recognition
Jingyun Jia, Philip K. Chan
Open set recognition (OSR) is the problem of classifying the known classes, meanwhile identifying the unknown classes when the collected samples cannot exhaust all the classes. The…
Learning a Neural-network-based Representation for Open Set Recognition
Mehadi Hassen, Philip K. Chan
Open set recognition problems exist in many domains. For example in security, new malware classes emerge regularly; therefore malware classification systems need to identify instan…