Papers › Temporal Generative Adversarial Nets with Singular Value Clipping
Temporal Generative Adversarial Nets with Singular Value Clipping
Masaki Saito, Eiichi Matsumoto, Shunta Saito
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.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Video Generation | UCF-101 16 frames, 64x64, Unconditional | TGAN-SVC | Inception Score | 11.85 | #6 of 7 | Archive leaderboard | report |
| Video Generation | UCF-101 16 frames, Unconditional, Single GPU | TGAN-SVC | Inception Score | 11.85 | #6 of 7 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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