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    An adversarial framework for op en-set human action recognition using skeleton data
    (Tubitak Scientific & Technological Research Council Turkey, 2021) Oztimur Karadag, Ozge
    Human action recognition is a fundamental problem which is applied in various domains, and it is widely studied in the literature. Majority of the studies model action recognition as a closed-set problem. However, in real-life applications it usually arises as an op en-set problem where a set of actions are not available during training but are introduced to the system during testing. In this study, we propose an op en-set action recognition system, human action recognition and novel action detection system (HARNAD), which consists of two stages and uses only 3D skeleton information. In the first stage, HARNAD recognizes a given action and in the second stage it decides whether the action really belongs to one of the a priori known classes or if it is a novel action. We evaluate the performance of the system experimentally both in terms of recognition and novelty detection. We also compare the system performance with state-of-the-art op en-set recognition methods. Our experiments show that HARNAD is compatible with state-of-the-art methods in novelty detection, while it is superior to those methods in recognition.
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    An Evaluation of the Layers of a Deep Network on the Optical Character Recognition Problem
    (Ieee, 2021) Saygin, Rahmani; Oztimur Karadag, Ozge
    Various layers of Convolutional Neural Networks, one of the most common methods of deep learning, have been examined by many researchers, and methods that will increase performance and reduce the complexity of computing have been proposed in classification using this architecture. In this paper, we investigate, over the Optical Character Recognition problem, which layers does the deep architecture owe its high performance in classification. For this purpose, we evaluated the effectiveness of the first layers of deep architecture by classifying the features extracted from deep architecture with a Support Vector Machine. Then, we evaluated the effects of these methods in classification by applying the Fully Connected Layer and Global Average Pooling Layer methods in the last layers of the deep neural network. Experiments pointed out that the deep network owes its performance to all of its layers, but alternative solutions on the upper layers of the architecture can reduce the computational complexity without a significant change in the performance..