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arXiv · 2210.00990

Visual Prompt Tuning for Generative Transfer Learning

Kihyuk Sohn·Yuan Hao·José Lezama·Luisa Polania·Huiwen Chang·Han Zhang·Irfan Essa·Lu Jiang
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Abstract

Transferring knowledge from an image synthesis model trained on a large dataset is a promising direction for learning generative image models from various domains efficiently. While previous works have studied GAN models, we present a recipe for learning vision transformers by generative knowledge transfer. We base our framework on state-of-the-art generative vision transformers that represent an image as a sequence of visual tokens to the autoregressive or non-autoregressive transformers. To adapt to a new domain, we employ prompt tuning, which prepends learnable tokens called prompt to the image token sequence, and introduce a new prompt design for our task. We study on a variety of visual domains, including visual task adaptation benchmark~\cite{zhai2019large}, with varying amount of training images, and show effectiveness of knowledge transfer and a significantly better image generation quality over existing works.

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Kihyuk Sohn, Yuan Hao, José Lezama, Luisa Polania, Huiwen Chang, Han Zhang, Irfan Essa, Lu Jiang. 2022-10-03. Visual Prompt Tuning for Generative Transfer Learning. https://arxiv.org/abs/2210.00990

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