Bytedance Researchers Propose CLIP-GEN: A New Self-Supervised Deep Learning Generative Approach Based On CLIP And VQ-GAN To Generate Reliable Samples From Text Prompts

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Synthesizing images from text has been a challenging topic in recent years. Early work, usually based on a convolutional generator that produces images directly from the given text, has shown promising results when working with limited domains; but, when extending the approach to the general domain, these methods have performed too poorly in terms of quality and image-test matching.

Recently, transformers have replaced convolution in text-image generation, and work such as OpenAI’s DALL-E has achieved significant improvements, mainly due …

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