Meta-learning few-shot image generation algorithm combining multi-head self-attention and convolution

Ze Zhang

doi:10.1117/12.2681078

23 May 2023 Meta-learning few-shot image generation algorithm combining multi-head self-attention and convolution

Ze Zhang

Proceedings Volume 12645, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023); 126451U (2023) https://doi.org/10.1117/12.2681078
Event: International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023), 2023, Hangzhou, China

Abstract

Generative Adversarial Network (GAN) has achieved remarkable results in the field of image generation, but it requires a large amount of image data to participate in the training, which is not expected in real-world work. Generating more images for this category in a new task with only a small amount of data has always been a challenging task, and a few-shot image generation is proposed to accomplish such tasks. This paper proposes a novel Generative Adversarial Network for few-shot image generation. The algorithm consists of two parts: a flexible Meta-model based on inner and outer loops, and a GAN combining CNN, Multi-Head Self-Attention (MHSA), and residuals for multi-tasking. Experimental results show that the algorithm achieves satisfactory results on several datasets, exploring the potential of the optimization-based approach for few-shot image generation.

Citation Download Citation

Ze Zhang "Meta-learning few-shot image generation algorithm combining multi-head self-attention and convolution", Proc. SPIE 12645, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023), 126451U (23 May 2023); https://doi.org/10.1117/12.2681078

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