Adversarial Robustness: Softmax versus Openmax

Deep neural networks (DNNs) provide state-of-the-art results on various tasks and are widely used in real world applications. However, it was discovered that machine learning models, including the best performing DNNs, suffer from a fundamental problem: they can unexpectedly and confidently misclassify examples formed by slightly perturbing otherwise correctly recognized inputs. Various approaches have been developed for efficiently generating these so-called adversarial examples, but those mostly rely on ascending the gradient of loss. In this paper, we introduce the novel logits optimized targeting system (LOTS) to directly manipulate deep features captured at the penultimate layer. Using LOTS, we analyze and compare the adversarial robustness of DNNs using the traditional Softmax layer with Openmax, which was designed to provide open set recognition by defining classes derived from deep representations, and is claimed to be more robust to adversarial perturbations. We demonstrate that Openmax provides less vulnerable systems than Softmax to traditional attacks, however, we show that it can be equally susceptible to more sophisticated adversarial generation techniques that directly work on deep representations.Comment: Accepted to British Machine Vision Conference (BMVC) 201

DOC: Deep Open Classification of Text Documents

Traditional supervised learning makes the closed-world assumption that the classes appeared in the test data must have appeared in training. This also applies to text learning or text classification. As learning is used increasingly in dynamic open environments where some new/test documents may not belong to any of the training classes, identifying these novel documents during classification presents an important problem. This problem is called open-world classification or open classification. This paper proposes a novel deep learning based approach. It outperforms existing state-of-the-art techniques dramatically.Comment: accepted at EMNLP 201

C2AE: Class Conditioned Auto-Encoder for Open-set Recognition

Models trained for classification often assume that all testing classes are known while training. As a result, when presented with an unknown class during testing, such closed-set assumption forces the model to classify it as one of the known classes. However, in a real world scenario, classification models are likely to encounter such examples. Hence, identifying those examples as unknown becomes critical to model performance. A potential solution to overcome this problem lies in a class of learning problems known as open-set recognition. It refers to the problem of identifying the unknown classes during testing, while maintaining performance on the known classes. In this paper, we propose an open-set recognition algorithm using class conditioned auto-encoders with novel training and testing methodology. In contrast to previous methods, training procedure is divided in two sub-tasks, 1. closed-set classification and, 2. open-set identification (i.e. identifying a class as known or unknown). Encoder learns the first task following the closed-set classification training pipeline, whereas decoder learns the second task by reconstructing conditioned on class identity. Furthermore, we model reconstruction errors using the Extreme Value Theory of statistical modeling to find the threshold for identifying known/unknown class samples. Experiments performed on multiple image classification datasets show proposed method performs significantly better than state of the art.Comment: CVPR2019 (Oral

Android multimedia program based on OpenMax

Cilj ovog rada bio je napraviti aplikaciju koja će na Android platformi putem OpenMax protokola pružati mogućnost reprodukcije multimedijskih sadržaja. Korištene tehnologije su Android, OpenMax i C programski jezik. Program je testiran na KAON BG2Q-4K razvojnoj ploči na kojoj je postavljen oprativni sustav Android verzija 5.1 Lolipop. U radu su predstavljene osnove rada sa Media servisom unutar operativnog sustavava Android te rad s OpenMax protkolom. U testovima je vidljivo kako gotovo nema razlike u korištenju OpenMax protokola.The aim of this work was to make the application for the Android platform which will through OpenMax protocol be able to play multimedia content. Technologies used are Android, OpenMax the C programming language. The program was tested on KAON BG2Q-4K evaluation board which is owned by the Institute RT-RK which had Android version 5.1 Lolipop. This paper presents the basics of working with the media services on the Android platform and work with OpenMax protocol. With results of tests we come to the conclusion that there is almost no difference in use of different protocols