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1. Twin Auxiliary Classifiers GAN

   Mingming Gong, Yanwu Xu (January 2019, Advances in neural information processing systems)

   null (Ed.)

   Conditional generative models enjoy remarkable progress over the past few years. One of the popular conditional models is Auxiliary Classifier GAN (AC-GAN), which generates highly discriminative images by extending the loss function of GAN with an auxiliary classifier. However, the diversity of the generated samples by AC-GAN tends to decrease as the number of classes increases, hence limiting its power on large-scale data. In this paper, we identify the source of the low diversity issue theoretically and propose a practical solution to solve the problem. We show that the auxiliary classifier in AC-GAN imposes perfect separability, which is disadvantageous when the supports of the class distributions have significant overlap. To address the issue, we propose Twin Auxiliary Classifiers Generative Adversarial Net (TAC-GAN) that further benefits from a new player that interacts with other players (the generator and the discriminator) in GAN. Theoretically, we demonstrate that TAC-GAN can effectively minimize the divergence between the generated and real-data distributions. Extensive experimental results show that our TAC-GAN can successfully replicate the true data distributions on simulated data, and significantly improves the diversity of class-conditional image generation on real datasets. 

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2. HGAN: Hybrid generative adversarial network

   https://doi.org/10.3233/JIFS-201202  

   Iranmanesh, Seyed Mehdi; Nasrabadi, Nasser M. (April 2021, Journal of Intelligent & Fuzzy Systems)

   In this paper, we present a simple approach to train Generative Adversarial Networks (GANs) in order to avoid a mode collapse issue. Implicit models such as GANs tend to generate better samples compared to explicit models that are trained on tractable data likelihood. However, GANs overlook the explicit data density characteristics which leads to undesirable quantitative evaluations and mode collapse. To bridge this gap, we propose a hybrid generative adversarial network (HGAN) for which we can enforce data density estimation via an autoregressive model and support both adversarial and likelihood framework in a joint training manner which diversify the estimated density in order to cover different modes. We propose to use an adversarial network to transfer knowledge from an autoregressive model (teacher) to the generator (student) of a GAN model. A novel deep architecture within the GAN formulation is developed to adversarially distill the autoregressive model information in addition to simple GAN training approach. We conduct extensive experiments on real-world datasets (i.e., MNIST, CIFAR-10, STL-10) to demonstrate the effectiveness of the proposed HGAN under qualitative and quantitative evaluations. The experimental results show the superiority and competitiveness of our method compared to the baselines. 

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3. DCG-GAN: design concept generation with generative adversarial networks

   https://doi.org/10.1017/dsj.2024.13  

   Ghasemi, Parisa; Yuan, Chenxi; Marion, Tucker; Moghaddam, Mohsen (January 2024, Design Science)

   Generative adversarial networks (GANs) have recently been proposed as a potentially disruptive approach to generative design due to their remarkable ability to generate visually appealing and realistic samples. Yet, we show that the current generator-discriminator architecture inherently limits the ability of GANs as a design concept generation (DCG) tool. Specifically, we conduct a DCG study on a large-scale dataset based on a GAN architecture to advance the understanding of the performance of these generative models in generating novel and diverse samples. Our findings, derived from a series of comprehensive and objective assessments, reveal that while the traditional GAN architecture can generate realistic samples, the generated and style-mixed samples closely resemble the training dataset, exhibiting significantly low creativity. We propose a new generic architecture for DCG with GANs (DCG-GAN) that enables GAN-based generative processes to be guided by geometric conditions and criteria such as novelty, diversity and desirability. We validate the performance of the DCG-GAN model through a rigorous quantitative assessment procedure and an extensive qualitative assessment involving 89 participants. We conclude by providing several future research directions and insights for the engineering design community to realize the untapped potential of GANs for DCG. 

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4. Bootstrapping Conditional GANs for Video Game Level Generation

   https://doi.org/10.1109/CoG47356.2020.9231576  

   Rodriguez Torrado, Ruben; Khalifa, Ahmed; Cerny Green, Michael; Justesen, Niels; Risi, Sebastian; Togelius, Julian (August 2020, IEEE Conference on Games)

   null (Ed.)

   Generative Adversarial Networks (GANs) have shown impressive results for image generation. However, GANs face challenges in generating contents with certain types of constraints, such as game levels. Specifically, it is difficult to generate levels that have aesthetic appeal and are playable at the same time. Additionally, because training data usually is limited, it is challenging to generate unique levels with current GANs. In this paper, we propose a new GAN architecture named Conditional Embedding Self-Attention Generative Adversarial Net- work (CESAGAN) and a new bootstrapping training procedure. The CESAGAN is a modification of the self-attention GAN that incorporates an embedding feature vector input to condition the training of the discriminator and generator. This allows the network to model non-local dependency between game objects, and to count objects. Additionally, to reduce the number of levels necessary to train the GAN, we propose a bootstrapping mechanism in which playable generated levels are added to the training set. The results demonstrate that the new approach does not only generate a larger number of levels that are playable but also generates fewer duplicate levels compared to a standard GAN. 

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5. ProgRPGAN: Progressive GAN for Route Planning

   https://doi.org/10.1145/3447548.3467406  

   Fu, Tao-yang; Lee, Wang-Chien (August 2021, Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2021))

   Learning to route has received significant research momentum as a new approach for the route planning problem in intelligent transportation systems. By exploring global knowledge of geographical areas and topological structures of road networks to facilitate route planning, in this work, we propose a novel Generative Adversarial Network (GAN) framework, namely Progressive Route Planning GAN (ProgRPGAN), for route planning in road networks. The novelty of ProgRPGAN lies in the following aspects: 1) we propose to plan a route with levels of increasing map resolution, starting on a low-resolution grid map, gradually refining it on higher-resolution grid maps, and eventually on the road network in order to progressively generate various realistic paths; 2) we propose to transfer parameters of the previous-level generator and discriminator to the subsequent generator and discriminator for parameter initialization in order to improve the efficiency and stability in model learning; and 3) we propose to pre-train embeddings of grid cells in grid maps and intersections in the road network by capturing the network topology and external factors to facilitate effective model learning. Empirical result shows that ProgRPGAN soundly outperforms the state-of-the-art learning to route methods, especially for long routes, by 9.46% to 13.02% in F1-measure on multiple large-scale real-world datasets. ProgRPGAN, moreover, effectively generates various realistic routes for the same query. 

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