DeepAI AI Chat
Log In Sign Up

MMF: A loss extension for feature learning in open set recognition

by   Jingyun Jia, et al.
Florida Institute of Technology

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. There are many applications for the OSR problem. For instance, the frequently emerged new malware classes require a system that can classify the known classes and identify the unknown malware classes. In this paper, we propose an add-on extension for loss functions in neural networks to address the OSR problem. Our loss extension leverages the neural network to find polar representations for the known classes so that the representations of the known and the unknown classes become more effectively separable. Our contributions include: First, we introduce an extension that can be incorporated into different loss functions to find more discriminative representations. Second, we show that the proposed extension can significantly improve the performances of two different types of loss functions on datasets from two different domains. Third, we show that with the proposed extension, one loss function outperforms the others in terms of training time and model accuracy.


page 3

page 4


Representation learning with function call graph transformations for malware open set recognition

Open set recognition (OSR) problem has been a challenge in many machine ...

Learning a Neural-network-based Representation for Open Set Recognition

Open set recognition problems exist in many domains. For example in secu...

Raman spectroscopy in open world learning settings using the Objectosphere approach

Raman spectroscopy in combination with machine learning has significant ...

Open Set Recognition with Conditional Probabilistic Generative Models

Deep neural networks have made breakthroughs in a wide range of visual u...

Reducing Network Agnostophobia

Agnostophobia, the fear of the unknown, can be experienced by deep learn...

Denoising Autoencoders for Overgeneralization in Neural Networks

Despite the recent developments that allowed neural networks to achieve ...

Spatial Location Constraint Prototype Loss for Open Set Recognition

One of the challenges in pattern recognition is open set recognition. Co...