Seeing What a GAN Cannot Generate (1910.11626v1)

Abstract: Despite the success of Generative Adversarial Networks (GANs), mode collapse remains a serious issue during GAN training. To date, little work has focused on understanding and quantifying which modes have been dropped by a model. In this work, we visualize mode collapse at both the distribution level and the instance level. First, we deploy a semantic segmentation network to compare the distribution of segmented objects in the generated images with the target distribution in the training set. Differences in statistics reveal object classes that are omitted by a GAN. Second, given the identified omitted object classes, we visualize the GAN's omissions directly. In particular, we compare specific differences between individual photos and their approximate inversions by a GAN. To this end, we relax the problem of inversion and solve the tractable problem of inverting a GAN layer instead of the entire generator. Finally, we use this framework to analyze several recent GANs trained on multiple datasets and identify their typical failure cases.

• The paper presents novel visualization techniques to quantify and understand the gap between generated and real data distributions.
• Methodologies like Generated Image Segmentation Statistics and Layer Inversion highlight specific object classes omitted by GANs.
• Experiments reveal that models like StyleGAN approximate real distributions better, yet persistent mode collapse challenges remain.

Understanding and Visualizing Mode Collapse in GANs: A Critical Analysis

Mode collapse is a well-documented challenge in the training of Generative Adversarial Networks (GANs), signifying the GAN's inability to capture the complete diversity of the target distribution. In the paper titled "Seeing What a GAN Cannot Generate," the authors delve into this issue by proposing novel methodologies to visualize and quantify mode collapse at both distribution and instance levels. The core contribution of this paper lies not in merely a numerical assessment of GAN quality but in understanding the qualitative discrepancies between generated and true data distributions.

The paper introduces two main methodologies to address mode collapse: Generated Image Segmentation Statistics and Layer Inversion. The first of these involves segmenting both generated and real images to measure the distribution of object classes. This method particularly emphasizes understanding which specific object classes are underrepresented in the output generated by GANs compared to the real distribution. For instance, in their experiments with a GAN trained on LSUN church images, the authors identify that objects such as people and fences are systematically omitted by the generator. Such insights are critical as they help understand the semantic voids in the generated data that numerical metrics like Inception Score or Fréchet Inception Distance (FID) fail to capture.

Layer Inversion is presented as an insightful technique to visualize specific instances of mode collapse. Through a judicious combination of a learned inversion network and layer-wise optimization, the method produces reconstructions of real images by inverting only a portion of the generator. This relaxation makes the inversion problem tractable and allows for identification of the content that a GAN cannot reconstruct. Notably, the paper finds that omitted object classes are not merely distorted or corrupted in output but are omitted altogether, thus highlighting blind spots in the generator's learned representation space.

The paper's analysis reveals meaningful differences in the generative performance of various GAN architectures. Among tested models, the approach shows that StyleGAN matches the real distribution more closely than systems like Progressive GAN or WGAN-GP. This indicates that some newer architectures might be more resistant to mode dropping, yet challenges persist even in state-of-the-art models.

The implications of this work are multifaceted. Practically, the ability to diagnostically analyze and visualize GAN outputs can lead to improved GAN training techniques by focusing on diversity rather than fidelity alone. Theoretically, these insights may guide the development of new GAN architectures capable of broader representation learning and thereby mitigate mode collapse.

Looking forward, the approaches described in the paper could inspire further research in several directions. How can GANs be enhanced to include neglected modes without resampling the training set or manually balancing the data? Can Layer Inversion be adapted for even larger networks or more complex generative models like those incorporating multimodal data? Addressing questions such as these could significantly broaden the applicability and reliability of GANs in generating realistic, diverse content.

In conclusion, this paper provides a significant advancement in the analysis of GAN behavior, offering tools to understand and visualize what lies outside the GAN's generative capacity. These contributions enable a nuanced understanding of mode collapse, propelling the field towards more inclusive generative modeling.