a graph is called textit{circulant} if it is a cayley graph on a cyclic group, i.e. its adjacency matrix is circulant. let $d$ be a set of positive, proper divisors of the integer $n>1$. the integral circulant graph $icg_{n}(d)$ has the vertex set $mathbb{z}_{n}$ and the edge set e$(icg_{n}(d))= {{a,b}; gcd(a-b,n)in d }$. let $n=p_{1}p_{2}cdots p_{k}m$, where $p_{1},p_{2},cdots,p_{k}$ are disti...

We present the first open-set language identification experiments using one-class classification models. We first highlight the shortcomings of traditional feature extractionmethods and propose a hashing-based feature vectorization approach as a solution. Using a dataset of 10 languages from different writing systems, we train a One-Class Support Vector Machine using only a monolingual corpus f...

AbstractOpen Set Video Anomaly Detection (OpenVAD) aims to identify abnormal events from video data where both known anomalies and novel ones exist in testing. Unsupervised models learned solely normal videos are applicable any testing but suffer a high false positive rate. In contrast, weakly supervised methods effective detecting could fail an open world. We develop method for the OpenVAD pro...

In open set recognition (OSR), almost all existing methods are designed specially for recognizing individual instances, even these instances collectively coming in batch. Recognizers decision either reject or categorize them to some known class using empirically-set threshold. Thus the threshold plays a key role. However, selection it usually depends on knowledge of classes, inevitably incurrin...

Many existing conditional Generative Adversarial Networks (cGANs) are limited to conditioning on pre-defined and fixed class-level semantic labels or attributes. We propose an open set GAN architecture (OpenGAN) that is conditioned per-input sample with a feature embedding drawn from metric space. Using state-of-the-art learning model encodes both fine-grained information, we able generate samp...

Incremental learning aims to learn new classes if they emerge while maintaining the performance for previously known classes. It acquires useful information from incoming data update existing models. Open-set recognition, however, requires ability recognize examples and reject new/unknown There are two main challenges in this matter. First, class discovery: algorithm needs not only but it must ...