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.

This paper presents a novel framework for 3D face reconstruction from single 2D images and addresses critical limitations in existing methods. Our approach integrates modified adversarial neural networks with graph neural networks to achieve state-of-the-art performance. Key innovations include (1) a generator architecture based on Graph Convolutional Networks (GCNs) with a novel loss function and identity blocks, mitigating mode collapse and instability; (2) the integration of facial landmarks and a non-parametric efficient-net decoder for enhanced feature capture; and (3) a lightweight GCN-based discriminator for improved accuracy and stability. Evaluated on the 300W-LP and AFLW2000-3D datasets, our method outperforms existing approaches, reducing Chamfer Distance by 62.7% and Earth Mover’s Distance by 57.1% on 300W-LP. Moreover, our framework demonstrates superior robustness to variations in head positioning, occlusion, noise, and lighting conditions while achieving significantly faster processing times.

Generative adversarial networks (GANs) are a powerful deep learning model for synthesizing realistic images; however, they can be difficult to train and are prone to instability and mode collapse. This paper presents a modified deep learning model called Identity Generative Adversarial Network (IGAN) to address the challenges of training and instability faced by generative adversarial models in synthesizing realistic images. The IGAN model includes three modifications to improve the performance of DCGAN: a non-linear identity block to ease complex data fitting and reduce training time; a modified loss function with label smoothing to smooth the standard GAN loss function; and minibatch training to use other examples from the same minibatch as side information for better quality and variety of generated images. The effectiveness of IGAN was evaluated and compared with other state-of-the-art generative models using the inception score (IS) and Fréchet inception distance (FID) on CelebA and stacked MNIST datasets. The experiments demonstrated that IGAN outperformed the other models in terms of convergence speed, stability, and diversity of results. Specifically, in 200 epochs, IGAN achieved an IS of 13.6 and an FID of 46.2. Furthermore, the IGAN collapsed modes were compared with other generative models using a stacked MNIST dataset, showing the superiority of IGAN in producing all the modes while the other models failed to do so. These results demonstrate that the modifications implemented in IGAN can significantly enhance the performance of GANs in synthesizing realistic images, providing a more stable, high-quality, and diverse output.