Temporal Generative Adversarial Nets with Singular Value Clipping (1611.06624v3)

Abstract: In this paper, we propose a generative model, Temporal Generative Adversarial Nets (TGAN), which can learn a semantic representation of unlabeled videos, and is capable of generating videos. Unlike existing Generative Adversarial Nets (GAN)-based methods that generate videos with a single generator consisting of 3D deconvolutional layers, our model exploits two different types of generators: a temporal generator and an image generator. The temporal generator takes a single latent variable as input and outputs a set of latent variables, each of which corresponds to an image frame in a video. The image generator transforms a set of such latent variables into a video. To deal with instability in training of GAN with such advanced networks, we adopt a recently proposed model, Wasserstein GAN, and propose a novel method to train it stably in an end-to-end manner. The experimental results demonstrate the effectiveness of our methods.

• The paper introduces a dual-generator architecture that separates temporal mapping from frame synthesis to improve video coherence.
• It adopts the Wasserstein GAN framework with Singular Value Clipping to enforce the Lipschitz constraint without manual tuning.
• Experimental results show that TGAN outperforms traditional 3D convolutional GANs in generating visually plausible and coherent video sequences.

Temporal Generative Adversarial Nets with Singular Value Clipping

The paper presented by Saito et al. introduces Temporal Generative Adversarial Nets (TGAN), a novel approach for video generation by leveraging unsupervised learning techniques. Addressing the intrinsic challenges associated with existing GAN-based video generation methodologies, this research integrates a temporal generator and an image generator within the GAN framework to capture temporal dynamics effectively.

Core Contributions

1. Dual-Generator Architecture: Unlike traditional GAN architectures for video generation that utilize a singular generator with 3D deconvolutional layers, TGAN incorporates two distinct sub-networks: a temporal generator and an image generator.
   • The temporal generator maps a single latent variable to multiple latent variables, each corresponding to a video frame.
   • The image generator uses these latent variables to synthesize the video frames.
2. Training Stability via Wasserstein GAN: The paper adopts the Wasserstein GAN (WGAN) framework to address the notorious instability issues in training GANs, owing to the complex interplay between the generator and discriminator.
3. Singular Value Clipping (SVC): A significant contribution is the introduction of Singular Value Clipping to enhance the stability of WGANs during training. Traditional clipping methods in WGANs involve hyperparameter sensitivity, while SVC ensures the Lipschitz constraint on the discriminator without the need for manual tuning of clipping values.
4. Frame Interpolation and Conditional Generation: The architecture naturally extends to frame interpolation applications, allowing smooth transitions between generated frames. Additionally, a conditional variant of TGAN is explored, where the generation process is influenced by provided categorical labels.

Experimental Findings

The authors conducted experiments on datasets like Moving MNIST, UCF-101, and a Golf scene dataset to demonstrate the model's efficacy. Notably, TGAN outperforms 3D convolutional GAN frameworks in generating coherent and visually plausible sequences. Quantitative evaluations via GAM scores and inception scores further underscore TGAN's superior performance, particularly highlighting its robustness even when labels are introduced in conditional TGAN.

Implications and Future Directions

The work on TGAN presents notable implications for the broader field of video generation. The introduction of a dual-generator system offers a framework that can be extended or adapted for more complex temporal sequences. The successful integration of SVC suggests avenues for exploring optimization frameworks in networks with intricate architectures. Future directions might include extending TGAN's utility to tasks such as video super-resolution and more intricate spatiotemporal modeling in videos with more complex dynamics. With the demonstrated benefits of SVC, further studies could refine this approach to improve GAN training across other domains beyond video generation.

In conclusion, this research makes substantial technical advancements in the training and structural aspects of GANs for video generation, providing a robust foundation for further explorations and applications in generative modeling within computer vision.