Learning by Competition: Dual Discriminator Generative Adversarial Networks

Baca, Carlos Andres Betancourt; Chen, Wenhui; Chen, Wei-Hao

doi:10.12792/icisip2018.022

Cited by 1 publication

(3 citation statements)

References 5 publications

(5 reference statements)

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“…Along with improvements in the quality of the images generated by GAN [18], [27], [28], a large number of papers [14], [28], [29], [30], [31] mention and attempt to mitigate the problem of mode collapse in GANs. But the mode collapse problem was seen as a secondary goal to work on that would be taken care of automatically as GAN optimization became more stable [25], [31], [32].…”

Section: Gan Mode Collapsementioning

confidence: 99%

“…recently many papers discussed the possibility of using multiple adversarial networks in GANs to enhance outcomes and reduce mode collapse [14], [29], [35], [36]. Ignoring the instability, they added by using multiple loss functions.…”

Section: Mitigating Mode Collapse Using Multi-discriminator and Multi...mentioning

confidence: 99%

“…MGAN [36] uses a mixture of generators. D2GAN [29] uses two discriminators. Parallel W-CGAN [14] which is shown in Fig.…”

Section: Mitigating Mode Collapse Using Multi-discriminator and Multi...mentioning

confidence: 99%

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Stabilizing and Improving Training of Generative Adversarial Networks Through Identity Blocks and Modified Loss Function

2023

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Generative adversarial networks (GANs) are a powerful tool for synthesizing realistic images, but they can be difficult to train and are prone to instability and mode collapse. This paper proposes a new model called Identity Generative Adversarial Network (IGAN) that addresses these issues. This model is based on three modifications to the baseline deep convolutional generative adversarial network (DCGAN). The first change is to add a non-linear identity block to the architecture. This will make it easier for the model to fit complex data types and cut down on the time it takes to train. The second change is to smooth out the standard GAN loss function by using a modified loss function and label smoothing. The third and final change is to use minibatch training to let the model use other examples from the same minibatch as side information to improve the quality and variety of generated images. These changes help to stabilize the training process and improve the model's performance. The performance of the GAN models is compared using the inception score (IS) and the Fréchet inception distance (FID), which are widely used metrics for evaluating the quality and diversity of generated images. The effectiveness of our approach was tested by comparing an IGAN model with other GAN models on the CelebA and stacked MNIST datasets. Results show that IGAN outperforms all the other models, achieving an IS of 13.95 and an FID of 43.71 after traning for 200 epochs. In addition to demonstrating the improvement in the performance of the IGAN, the instabilities, diversity, and fidelity of the models were investigated. The results showed that the IGAN was able to converge to a distribution of the real data more quickly. Furthermore, the experiments revealed that IGAN is capable of producing more stable and high-quality images. This suggests that IGAN is a promising approach for improving the training and performance of GANs and may have a range of applications in image synthesis and other areas.INDEX TERMS Generative adversarial network, deep learning, mode collapse, label smoothing, identity block.

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