Co-Representation Learning Framework For the Open-Set Data Classification

Deep Neural Network (DNN) has been largely demonstrated to be effective for real-world classification problems. However, such model requires a huge amount of training samples to get more accurate result. When limited samples allowed for the training step, the model may perform weak generalization ability on the test set, especially when the novel/unseen class may occur during the test period (we call it open-set classification). This severely limits its further utility in many real-world large scale applications, such as the open-set image and text classification scenarios. In this paper, we focus on addressing this key challenge by developing a DNN based co-representation learning approach RLCN. It utilizes limited samples for training a model then applies it to classify normal instances and detect the emergence of novel class over time. The key novelty is that we design a weighted pairwise-constraint loss (WPC) function to learn an enhanced generalization and robust feature embedding, where the intra-class (same class) compactness and inter-class (different class) separation are achieved. Moreover, we apply the temperature scaling scheme on the softmax function to replace traditional softmax output in our open-world classifier to achieve the classification and novel class detection simultaneously. Our extensive empirical evaluation on benchmark datasets demonstrate the effectiveness of our framework compared to other competing techniques.