P-ODN: Prototype-based Open Deep Network for Open Set Recognition
نویسندگان
چکیده
منابع مشابه
Learning a Neural-network-based Representation for Open Set Recognition
Open set recognition problems exist in many domains. For example in security, new malware classes emerge regularly; therefore malware classication systems need to identify instances from unknown classes in addition to discriminating between known classes. In this paper we present a neural network based representation for addressing the open set recognition problem. In this representation insta...
متن کاملTowards Open Set Deep Networks: Supplemental
In this supplement, we provide we provide additional material to further the reader as understanding of the work on Open Set Deep Networks, Mean Activation Vectors, Open Set Recognition and OpenMax algorithm. We present additional experiments on ILSVRC 2012 dataset. First we present experiments to illustrate performance of OpenMax for various parameters of EVT calibration (Alg. 1, main paper) f...
متن کاملOpen-Set Face Recognition-Based Visitor Interface System
This work presents a real-world, real-time video-based open-set face recognition system. The system has been developed as a visitor interface, where a visitor looks at the monitor to read the displayed message before knocking on the door. While the visitor is reading the welcome message, using the images captured by the webcam located on the screen, the developed face recognition system identif...
متن کاملDeep Learning-Based Goal Recognition in Open-Ended Digital Games
While many open-ended digital games feature non-linear storylines and multiple solution paths, it is challenging for game developers to create effective game experiences in these settings due to the freedom given to the player. To address these challenges, goal recognition, a computational player-modeling task, has been investigated to enable digital games to dynamically predict players’ goals....
متن کاملReserve Output Units for Deep Open-set Learning
Open-set learning poses a classification problem where the set of class labels expands over time; a realistic but not widely-studied setting. We propose a deep learning technique for open-set learning based on reserve output units (ROUs), which are designed to help a network anticipate the introduction of new categories during training. ROUs are additional output units whose representations are...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Scientific Reports
سال: 2020
ISSN: 2045-2322
DOI: 10.1038/s41598-020-63649-6