Mode Seeking Generative Adversarial Networks for Diverse Image Synthesis (1903.05628v6)

Abstract: Most conditional generation tasks expect diverse outputs given a single conditional context. However, conditional generative adversarial networks (cGANs) often focus on the prior conditional information and ignore the input noise vectors, which contribute to the output variations. Recent attempts to resolve the mode collapse issue for cGANs are usually task-specific and computationally expensive. In this work, we propose a simple yet effective regularization term to address the mode collapse issue for cGANs. The proposed method explicitly maximizes the ratio of the distance between generated images with respect to the corresponding latent codes, thus encouraging the generators to explore more minor modes during training. This mode seeking regularization term is readily applicable to various conditional generation tasks without imposing training overhead or modifying the original network structures. We validate the proposed algorithm on three conditional image synthesis tasks including categorical generation, image-to-image translation, and text-to-image synthesis with different baseline models. Both qualitative and quantitative results demonstrate the effectiveness of the proposed regularization method for improving diversity without loss of quality.

An Analysis of Mode Seeking Generative Adversarial Networks for Diverse Image Synthesis

The paper, "Mode Seeking Generative Adversarial Networks for Diverse Image Synthesis," introduces an innovative approach to address the persistent issue of mode collapse in Conditional Generative Adversarial Networks (cGANs). The authors propose a straightforward, yet effective, regularization method to enhance both the diversity and the quality of the outputs generated by cGANs across a range of conditional image synthesis tasks.

Problem Statement

Conditional GANs have gained popularity for their ability to generate images conditioned on various contexts, such as class labels, text descriptions, or other images. However, a significant challenge faced by cGANs is mode collapse, where the generator consistently produces outputs from a limited subset of the possible distribution modes, thereby reducing the diversity of the generated samples. This issue is particularly troublesome in multimodal tasks where diverse output is crucial.

Methodology

To mitigate the mode collapse problem, the authors introduce a mode-seeking regularization term. This term maximizes the ratio between the distance of generated images and their respective latent codes. By doing so, the generator is encouraged to explore multiple modes in the image space, thereby increasing output diversity without incurring additional computational burdens or requiring structural changes to the network. The method is evaluated on tasks such as categorical image generation, image-to-image translation, and text-to-image synthesis.

Experimental Results

The authors validate their approach using three benchmark tasks with various baseline models:

1. Categorical Image Generation: With the CIFAR-10 dataset and the DCGAN model, the proposed method demonstrated improved diversity, measured through metrics like NDB and JSD, without sacrificing image quality as assessed by the Fréchet Inception Distance (FID).
2. Image-to-Image Translation: The application of the method to Pix2Pix and DRIT showcases its ability to enhance diversity in both paired (e.g., facades, maps) and unpaired (e.g., Yosemite summer-to-winter translations) datasets. The results indicated superior diversity alongside maintained visual fidelity.
3. Text-to-Image Synthesis: By incorporating the regularization term into StackGAN++ coupled with the CUB-200-2011 dataset, the method successfully increased diversity, as evidenced by perceptual distance metrics, while preserving the similarity to real data distribution.

Key Contributions

The research offers several key contributions to the field:

• Introduction of a mode-seeking regularization that generalizes across a range of cGAN frameworks with minimal computational overhead.
• Empirical evidence demonstrating improved output diversity on challenging conditional image synthesis tasks while maintaining image quality.
• Adaptability of the proposed regularization term across multiple generative tasks and baseline architectures without the need for additional auxiliary networks or modifications.

Implications and Future Work

The paper underscores the efficacy of simple yet strategic modifications within generative models to tackle long-standing issues like mode collapse. The proposed technique holds potential for widespread application in diverse generative tasks, particularly those demanding high variability in outputs. Future research could further explore the integration of this regularization method with emerging types of GANs and other generative models in different domains, such as video synthesis or 3D object generation.

Overall, this paper contributes a valuable technique for enhancing the functionality and applicability of cGANs, potentially broadening the scope and impact of generative models in artificial intelligence.