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University of Electronic Science and Technology of China
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Dodging generator-specific fingerprints is now possible: FGINet leverages frequency-aware gating to fuse semantic and artifact cues, significantly boosting generalization in AI-generated image detection.
Existing synthetic image detectors fail to generalize to new generators, but HiMix closes the gap by learning artifact-aware representations across diverse distributions.
Skewed item distributions in recommendation systems can be tamed with a learnable non-uniform quantization, leading to better codebook utilization and more accurate generative recommendations.
