AE-OT-GAN: Training GANs from Data Specific Latent Distribution

An, Dongsheng; Guo, Yi; Zhang, Min; Qi, Xin; Lei, Na; Gu, Xianfang

doi:10.1007/978-3-030-58574-7_33

Cited by 13 publications

(5 citation statements)

References 13 publications

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“…Thus, Dist-GAN slows down the convergence of the discriminator and alleviates gradient vanishing effectively. An et al [48] designed AE-OT-GAN, which consists of an auto-encoder and a discriminator, to alleviate the mode collapse problem in the image generation field. In AE-OT-GAN, the AE and GAN share the same module, which plays the role of the decoder and generator in the AE and GAN, respectively.…”

Section: Combination Of Gan and Aementioning

confidence: 99%

3D Point Cloud Shape Generation with Collaborative Learning of Generative Adversarial Network and Auto-Encoder

Yang,

Wang,

Yang

2024

Remote Sensing

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A point cloud is a simple and concise 3D representation, but point cloud generation is a long-term challenging task in 3D vision. However, most existing methods only focus on their effectiveness of generation and auto-encoding separately. Furthermore, both generative adversarial networks (GANs) and auto-encoders (AEs) are the most popular generative models. But there is a lack of related research that investigates the implicit connections between them in the field of point cloud generation. Thus, we propose a new bidirectional network (BI-Net) trained with collaborative learning, introducing more priors through the alternate parameter optimizations of a GAN and AE combination, which is different from the way of combining them at the network structure and loss function level. Specifically, BI-Net acts as a GAN and AE in different data processing directions, where their network structures can be reused. If optimizing only the GAN without the AE, there is no direct constraint of ground truth on the generator’s parameter optimization. This unique approach enables better network optimization and leads to superior generation results. Moreover, we propose a nearest neighbor mutual exclusion (NNME) loss to further homogenize the spatial distribution of generated points during the reverse direction. Extensive experiments were conducted, and the results show that the BI-Net produces competitive and high-quality results on reasonable structure and uniform distributions compared to existing state-of-the-art methods. We believe that our network structure (BI-Net) with collaborative learning could provide a new promising method for future point cloud generation tasks.

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Section: Combination Of Gan and Aementioning

confidence: 99%

3D Point Cloud Shape Generation with Collaborative Learning of Generative Adversarial Network and Auto-Encoder

Yang,

Wang,

Yang

2024

Remote Sensing

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“…This formulation leads to the generative model [132]. (Moreover, a few models with some technical variations [4,5,98] are related to this category, but for simplicity we do not describe them here. )…”

Section: Combinationmentioning

confidence: 99%

A Mathematical Framework for Learning Probability Distributions

Yang¹

2022

JML

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The modeling of probability distributions, specifically generative modeling and density estimation, has become an immensely popular subject in recent years by virtue of its outstanding performance on sophisticated data such as images and texts. Nevertheless, a theoretical understanding of its success is still incomplete. One mystery is the paradox between memorization and generalization: In theory, the model is trained to be exactly the same as the empirical distribution of the finite samples, whereas in practice, the trained model can generate new samples or estimate the likelihood of unseen samples. Likewise, the overwhelming diversity of distribution learning models calls for a unified perspective on this subject. This paper provides a mathematical framework such that all the well-known models can be derived based on simple principles. To demonstrate its efficacy, we present a survey of our results on the approximation error, training error and generalization error of these models, which can all be established based on this framework. In particular, the aforementioned paradox is resolved by proving that these models enjoy implicit regularization during training, so that the generalization error at early-stopping avoids the curse of dimensionality. Furthermore, we provide some new results on landscape analysis and the mode collapse phenomenon.

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“…Optimal transport (OT) is a powerful tool to compute the Wasserstein distance between probability measures and widely used to model various natural and social phenomena, including economics (Galichon 2016), optics (Glimm and Oliker 2003), biology (Schiebinger et al 2019), physics (Jordan, Kinderlehrer, and Otto 1998) and in other scientific fields. Recently, OT has been successfully applied in machine learning and statistics, such as parameter estimation in Bayesian non-parametric models (Nguyen 2013), computer vision (Arjovsky, Chintala, and Bottou 2017;Courty et al 2017;Tolstikhin et al 2018;An et al 2020a;Lei et al 2020;An et al 2020b), and natural language processing (Kusner et al 2015;Yurochkin et al 2019). In these areas, the complex probability measures are approximated by summations of Dirac measures supported on the samples.…”

Section: Introductionmentioning

confidence: 99%

Efficient Optimal Transport Algorithm by Accelerated Gradient Descent

Lei

2022

AAAI

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Optimal transport (OT) plays an essential role in various areas like machine learning and deep learning. However, computing discrete optimal transport plan for large scale problems with adequate accuracy and efficiency is still highly challenging. Recently, methods based on the Sinkhorn algorithm add an entropy regularizer to the prime problem and get a trade off between efficiency and accuracy. In this paper, we propose a novel algorithm to further improve the efficiency and accuracy based on Nesterov's smoothing technique. Basically, the non-smooth c-transform of the Kantorovich potential is approximated by the smooth Log-Sum-Exp function, which finally smooths the original non-smooth Kantorovich dual functional. The smooth Kantorovich functional can be optimized by the fast proximal gradient algorithm (FISTA) efficiently. Theoretically, the computational complexity of the proposed method is lower than current estimation of the Sinkhorn algorithm in terms of the precision. Empirically, compared with the Sinkhorn algorithm, our experimental results demonstrate that the proposed method achieves faster convergence and better accuracy with the same parameter.

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AE-OT-GAN: Training GANs from Data Specific Latent Distribution

Cited by 13 publications

References 13 publications

3D Point Cloud Shape Generation with Collaborative Learning of Generative Adversarial Network and Auto-Encoder

3D Point Cloud Shape Generation with Collaborative Learning of Generative Adversarial Network and Auto-Encoder

A Mathematical Framework for Learning Probability Distributions

Efficient Optimal Transport Algorithm by Accelerated Gradient Descent

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