Summary of Video to Video Generative Adversarial Network For Few-shot Learning Based on Policy Gradient, by Yintai Ma et al.
Video to Video Generative Adversarial Network for Few-shot Learning Based on Policy Gradient
by Yintai Ma, Diego Klabjan, Jean Utke
First submitted to arxiv on: 28 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed RL-V2V-GAN model combines deep reinforcement learning and generative adversarial networks to achieve sophisticated video-to-video synthesis. The medium-difficulty summary will explore how this model, unlike traditional methods requiring paired inputs, can learn a mapping between source and target video domains without requiring paired data. We’ll examine the ConvLSTM layers that capture spatial and temporal information, as well as the fine-grained GAN architecture and spatio-temporal adversarial goals designed to preserve style while translating content. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The RL-V2V-GAN model allows for more general video-to-video synthesis without requiring paired inputs. This makes it effective in situations where there are limited videos in the target domain, such as few-shot learning. The model produces temporally coherent video results and highlights the potential for further advances in this area. |
Keywords
» Artificial intelligence » Few shot » Gan » Reinforcement learning
