Generative Adversarial Networks (GANs): Challenges, Solutions, and Future Directions

Generative Adversarial Networks (GANs) is a novel class of deep generative models which has recently gained significant attention. GANs learns complex and high-dimensional distributions implicitly over images, audio, and data. However, there exists major challenges in training of GANs, i.e., mode collapse, non-convergence and instability, due to inappropriate design of network architecture, use of objective function and selection of optimization algorithm. Recently, to address these challenges, several solutions for better design and optimization of GANs have been investigated based on techniques of re-engineered network architectures, new objective functions and alternative optimization algorithms. To the best of our knowledge, there is no existing survey that has particularly focused on broad and systematic developments of these solutions. In this study, we perform a comprehensive survey of the advancements in GANs design and optimization solutions proposed to handle GANs challenges. We first identify key research issues within each design and optimization technique and then propose a new taxonomy to structure solutions by key research issues. In accordance with the taxonomy, we provide a detailed discussion on different GANs variants proposed within each solution and their relationships. Finally, based on the insights gained, we present the promising research directions in this rapidly growing field.Comment: 42 pages, Figure 13, Table

Word Recognition with Deep Conditional Random Fields

Recognition of handwritten words continues to be an important problem in document analysis and recognition. Existing approaches extract hand-engineered features from word images--which can perform poorly with new data sets. Recently, deep learning has attracted great attention because of the ability to learn features from raw data. Moreover they have yielded state-of-the-art results in classification tasks including character recognition and scene recognition. On the other hand, word recognition is a sequential problem where we need to model the correlation between characters. In this paper, we propose using deep Conditional Random Fields (deep CRFs) for word recognition. Basically, we combine CRFs with deep learning, in which deep features are learned and sequences are labeled in a unified framework. We pre-train the deep structure with stacked restricted Boltzmann machines (RBMs) for feature learning and optimize the entire network with an online learning algorithm. The proposed model was evaluated on two datasets, and seen to perform significantly better than competitive baseline models. The source code is available at https://github.com/ganggit/deepCRFs.Comment: 5 pages, published in ICIP 2016. arXiv admin note: substantial text overlap with arXiv:1412.339