Diversity-Sensitive Conditional Generative Adversarial Networks (1901.09024v1)

Abstract: We propose a simple yet highly effective method that addresses the mode-collapse problem in the Conditional Generative Adversarial Network (cGAN). Although conditional distributions are multi-modal (i.e., having many modes) in practice, most cGAN approaches tend to learn an overly simplified distribution where an input is always mapped to a single output regardless of variations in latent code. To address such issue, we propose to explicitly regularize the generator to produce diverse outputs depending on latent codes. The proposed regularization is simple, general, and can be easily integrated into most conditional GAN objectives. Additionally, explicit regularization on generator allows our method to control a balance between visual quality and diversity. We demonstrate the effectiveness of our method on three conditional generation tasks: image-to-image translation, image inpainting, and future video prediction. We show that simple addition of our regularization to existing models leads to surprisingly diverse generations, substantially outperforming the previous approaches for multi-modal conditional generation specifically designed in each individual task.

Diversity-sensitive Conditional Generative Adversarial Networks: A Critical Analysis

The paper "Diversity-sensitive Conditional Generative Adversarial Networks" addresses one of the perennial challenges in Conditional Generative Adversarial Networks (cGANs): mode collapse. This phenomenon occurs when the generator of a GAN produces a limited variety of outputs, failing to capture the full diversity present in the training data.

Problem Statement and Contribution

Conditional GANs have been extensively applied to a range of tasks including image-to-image translation, image inpainting, and future video prediction. However, they often struggle with the mode collapse problem, especially when the input and output data are high-dimensional, as is typical in images and videos. The mode collapse issue is exacerbated in cGANs because the generator tends to create deterministic outputs for given inputs, sidelining stochasticity that could lead to more diverse results.

The authors propose a novel approach to counteract this tendency by integrating a diversity-sensitive regularization term directly into the cGAN's objective. This method encourages the generator to produce varied outputs by altering its adversarial training regimen. The proposed technique maintains the generator's focus on realism while allowing it to explore a broader range of outputs. The key contributions of the paper include:

• Simplicity and General Applicability: The regularization method does not necessitate changes to network architecture and can be seamlessly integrated into most existing cGANs.
• Controllable Diversity: By explicitly introducing a diversity-enforcing term in the objective function, it allows for a tunable balance between visual quality and diversity through a hyperparameter.
• Broad Applicability Across Tasks: Demonstrated efficacy across a broad spectrum of conditional generation tasks, showing unexpected diversity gains in image-to-image translation, image inpainting, and video prediction.

Methodology

The proposed method involves augmenting the cGAN's objective function with a diversity regularization term. This term pressures the generator to diversify its outputs based on variations in the latent space. Specifically, the introduced regularization maximizes the Euclidean distance between outputs mapped from different latent codes, thus fostering a one-to-one mapping strategy rather than a many-to-one pattern.

Mathematically, this is represented by a maximization term appended to the generator's loss, which is parametrized by a hyperparameter $\lambda$. This parameter not only offers control over the degree of stochasticity but also influences generator outputs' visual quality.

Empirical Evaluation

The authors conduct empirical studies on three conditional generation tasks with notable improvements:

1. Image-to-Image Translation: By adding the proposed regularization, models surpass both traditional cGANs and specialized models like BicycleGAN, notably in LPIPS diversity scores and FID metrics. The diversity effects are clear, particularly in tasks like edges$\rightarrow$photo translations.
2. Image Inpainting: Utilizing a feature space distance metric enhances semantic diversity, yielding recognizable variations in facial attributes without sacrificing coherence with the given data context.
3. Video Prediction: The method effectively applies to sequence data, outperforming SAVP in measures of diversity while retaining high similarity to ground-truth sequences.

Implications and Future Work

Theoretical and practical implications of this work are profound. Theoretically, it offers a fresh perspective on balancing adversarial dynamics with generator exploration in high-dimensional latent spaces. Practically, it enables enhancements in applications where capturing data diversity conveys significant aesthetic or functional improvements, such as creative domains or predictive modeling.

Future research directions could delve into adaptive learning of the $\lambda$ hyperparameter to optimize diversity-realism trade-offs autonomously. Moreover, extending the method to unsupervised GANs could provide further insights into unconditional data synthesis.

In summary, this work provides a meaningful advancement in addressing mode collapse in cGANs, paving the way for producing more diverse and realistic generative models. While not without limitations, as noted in the discussions regarding the trade-off between visual quality and diversity, the simplicity and effectiveness of the approach highlight its potential for widespread application.