Researchers from China Unveil ImageReward: A Groundbreaking Artificial Intelligence Approach to Optimizing Text-to-Image Models Using Human Preference Feedback

Recent years have seen tremendous developments in text-to-image generative models, including auto-regressive and diffusion-based methods. These models can produce high-fidelity, semantically relevant visuals on various topics when given the right language descriptions (i.e., prompts), sparking considerable public interest in their possible uses and effects. Despite the advancements, current self-supervised pre-trained generators still have a long way to go. Since the pre-training distribution is noisy and different from the actual user-prompt distributions, aligning models with human preferences is a major difficulty.  The resulting difference causes several well-known problems in the photographs, including but not limited to: • Text-image alignment errors: as

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