Faster Projected GAN: Improved Few-Shot Image Generation Model
Conceptos Básicos
Improved Faster Projected GAN model accelerates training speed and reduces memory usage while maintaining image quality.
Resumen
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1. Abstract:
Proposed Faster Projected GAN model based on Projected GAN.
Focuses on improving generator with depth separable convolution (DSC).
Achieved 20% speed increase and 15% memory saving in experiments.
2. Introduction:
Images crucial for various fields.
Generative Adversarial Networks (GAN) significant for image generation.
Few-shot learning essential for AI development.
3. Related Work:
GANs have shown progress in deep learning.
Various models like Matching GAN, F2GAN, LoFGAN used for few-shot image generation.
4. Network Structure of Faster Projected GAN:
Improvement focused on Generator using DSC.
DSC not effective on Discriminator, maintained original structure.
5. Ablation:
Comparative experiment showed different effects of DSC module on Generator and Discriminator.
6. Experimental Analysis and Evaluation:
Faster Projected GAN demonstrated improvement over state-of-the-art models in terms of FID loss and training time.
7. Conclusion:
Proposed Faster Projected GAN combines depth-separable convolution to accelerate training and save memory while ensuring image quality.
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arxiv.org
Faster Projected GAN
Estadísticas
提案されたモデルは、実験において20%の速度向上と15%のメモリ節約を達成しました。
Citas
If one party is too strong, it will cause the model to collapse.
Using depthwise separable convolutions can significantly reduce the computational burden and model parameters.